Download THE INFLUENCE OF THE AFRICAN ELEPHANT (LOXODONTA

THE INFLUENCE OF THE AFRICAN ELEPHANT
(LOXODONTA
AFRICANA)
ON
THE
WOODY
VEGETATION LAYER IN THE SEMI-ARID SAVANNA
BIOME –
AN ASSESSMENT OF THE LATE DRY SEASON
FEEDING HABITS AND PREFERENCES AND THEIR
IMPACT ON CHANGES IN VEGETATION COMMUNITY
COMPOSITION AND STRUCTURE
JULIA-SASCHA WEBER
MSc WILDLIFE BIOLOGY AND CONSERVATION
School of Life, Sport and Social Science
Edinburgh Napier University
February 2014
Indlovu ~
This is for you
May your rumbles be heard forever
ACKNOWLEDEGEMENTS
First and foremost, I would like to thank my parents. Thank you to my dad for introducing
me to the unique continent of Africa more than two decades ago. You were right Daddy, it is
addictive. Thank you to my mama for always making peace. Thank you both for believing in
me.
I am exceptionally indebted to my supervisors, Dr. Michelle Henley and Dr. Paul Ward. Your
continuous support, your expert advice and your utmost patience guided me through my time
in Scotland and South Africa.Your trust in me and my work means a lot to me, and for this I
thank you!
My deepest expression of gratitude goes to you, Michelle, who taught me how to talk to
ducks and much more… The elephants need you, so please, never give up!
I am extremely grateful to Craig Spencer, Stefan Bosman and Transfrontier Africa for
providing expertise, transportation, housing and the best camp fires. Thank you Craig! You
guys made me laugh, cry and rage but without you this project would not have been possible.
Craig, you have to continue the fight, there is no time for retirement…
An extraordinary and big thank-you to Glen Thomson, Francois Oberholzer and the whole of
JPNR. You jumped on board when my original project crumbled to pieces. Your enthusiasm
about taking part in the research on elephants was amazing and saved this project. Thank you,
Glen!
Thank you to all the landowners on JPNR. You welcomed me with smiles every day and did
not get tired of expressing your interest in my work. Thank you!
Thank you to all the landowners and lodges that made my field work possible on OWNR! A
special thank-you goes to Günter Reinstorf, who readily shared information about the
Reserve’s history, provided interesting facts and figures and happily answered my questions.
Thank you Günter!
An exclusive thank-you and big hug to Helen Buxton for your devoted support and advice.
Thanks Helen!
And, last but not least, a very big thank-you to all the Transfrontier Africa Volunteers who
shared my passion and fascination for elephants, who crawled through the grewia hell on
their knees, who literally got hooked by our beloved knob thorns and who devoted their time
and money to conservation. Thanks, guys!
i
ABSTRACT
The African elephant (Loxodonta africana) - keystone species, ecosystem engineer and the cause
of a global debate. A debate generated by the ecological and aesthetic concerns of managers and
landowners in the face of increasing elephant populations in Southern Africa’s protected areas.
These concerns predominantly result from the way elephants interact with their habitat by
utilizing the woody vegetation layer as their resource base, a foraging behaviour that is
particularly pronounced in the dry season when resources become limited. The general alteration
of vegetation structure and community, the extinction of certain valued plant species, the
exhaustion of the resource foundation for other herbivorous species and the depletion of aesthetic
landscape characteristics, in particular of large trees, are the prime worries fuelling the debate.
Culling, translocation and contraception have been considered, tested, dismissed and discussed
again as potential solutions. An alternative that has been promoted is ‘Space’. The creation of
‘Transfrontier Megaparks’ and the aspired return of metapopulation dynamics are thought to
encourage dispersal, therefore leading to localized impact relaxation while allowing for
regeneration windows. Nature Reserves however, which are joining the large network of source
and sink subpopulations, are inevitably functioning as ‘dispersal sinks’ and report an initial and
noticeable influx of elephants and intensive utilization of the vegetation.
A comparative evaluation was conducted during the late dry season on two Nature Reserves
(Olifants West Nature Reserve and Jejane Private Nature Reserve) that joined the Greater Kruger
National Park within eight years of each other, thereby developing a distinct history of habitat
utilization by elephants. In order to provide important information on habitat conditions and the
elephants’ feeding ecology, thereby aiming to allow for a better understanding that will help
decisions with regards to management and conservation of a megaherbivore, this study had the
following three main objectives: 1. To create a ‘benchmark’ data base of the vegetation structure
and community, thereby enabling a better comprehension of the current vegetation state of the
two reserves, as well as to provide reference data for future monitoring programmes. 2. To give
insight into the general feeding behaviour of the African elephant and its effect on the woody
vegetation layer during the late dry season. 3. To provide information on potential sex-related
feeding habits.
Two different survey methods were applied. Vegetation plots set up along permanent transects
were sampled biweekly for any impact resulting from the utilization by elephants. In order to
record feeding behaviour, a fixed ‘elephant route’ was driven two days a week on each reserve in
the search of elephants, and plant-based surveys of food plots along fresh (<12 hours) feeding
paths conducted as often as the opportunity arose.
ii
Vegetation structure, species richness, composition and diversity were found to be comparable
between study sites. The population structure of Sclerocarya birrea (marula) on both reserves
proved to be unstable with a major recruitment failure of at least 60 years, while results indicated
a more stable Acacia nigrescens (knob thorn) population which appeared to be able to persist.
Selective feeding on the plant species level was confirmed in that, of all 30 species that were
sampled during the late dry season, a subset of eight species made up 95% of the elephant diet on
both study sites and, within this narrow range, certain species were preferred to others. Shrubs
between 1m and 2.5m in height and trees above 4.5m were the selected target. Utilization of
distinct height classes was however recorded to be in relation to availability. Foraging decisions
were furthermore made at the plant part level, with 59% of the diet comprising roots and bark,
indicating preferences for nutrient storing tissues during the late dry season.
In accordance with the ‘Body Size Hypothesis’, used as the application of the Jarman-Bell
principle at intraspecific scale, selectivity and foraging decisions of the African elephant, as a
sexually dimorphic species, should differ between sexes. Due to the higher mass specific
metabolic requirements that result from their smaller body size and reproductive demands, family
units were predicted to be more selective at plant species and plant part level than bulls. No sexrelated feeding behaviour proved to be significant at either level during this study. It was
suggested that relatively low elephant densities and excellent veld conditions at the time of
sampling might have resulted in browse availability above levels at which competitive
displacement among sexes would have been likely to set in, as high quality food was still
sufficiently available and low quality food abundant, therefore allowing both sexes to feed on
high nutrient sources. The lack of sex-related feeding behaviour was furthermore considered to be
a consequence of the fact that the majority of recorded bulls were young adults, with a body size
resembling more that of adult females than of mature bulls, therefore resulting in similar energy
requirements and hence feeding patterns.
It was recommended that one should consider multiple variables when evaluating the system’s
condition and the potential consequences of elephant feeding habits over time. Former land-use
practices, general herbivory and the lack of a historical and ‘natural benchmark’ with regard to
vegetation structure and community all appeared to be essential.
The consideration of the
savanna ecosystem as a dynamic system rather than a steady state was furthermore proposed to be
fundamental.
iii
CONTENTS
ACKNOWLEGEMENTS
i
ABSTRACT
ii
LIST OF FIGURES
vii
LIST OF TABLES
ix
Chapter 1
The African elephant (Loxodonta africana), ‘landscape architect’
and ‘ecosystem engineer’ – A general introduction to its feeding
ecology and habitat use and the potential consequences for
ecosystem and biodiversity
1.1
Introduction
1
1.2
Objectives and outcomes
8
1.3
Thesis structure
9
1.4
References
10
Chapter 2
Study area and methods
2.1
Study area
1
2.2
History of elephant distribution in the Greater Kruger National Park
3
2.3
Methods
4
Definitions, sampling design and data collection
2.4
2.3.1 Data analysis
4
2.3.2 Tree – shrub definition
4
2.3.3 Sampling design and data collection
5
2.3.3.1 Transect sampling
5
2.3.3.2 Plant-based surveys of food plots along fresh feeding paths
7
References
9
iv
Chapter 3
3.1
Vegetation structure and composition of two Nature Reserves
located within the semi-arid savanna biome
Introduction
1
3.1.1 Objectives
3
3.2
Methods
4
3.3
Data analysis
4
3.3.1 Vegetation structure
4
3.3.2 Vegetation composition
5
Results
7
3.4.1 Vegetation structure
7
3.4.2 Vegetation composition
10
3.5
Discussion
14
3.6
Summarizing conclusion and implications
23
3.7
References
25
3.4
Chapter 4
4.1
General Feeding Ecology of the African Elephant (Loxodonta africana)
Introduction
1
4.1.1 Objectives
6
4.2
Methods
7
4.3
Data analysis
7
4.3.1 Woody plant species – availability and utilization
7
4.3.2 Utilization of trees and shrubs
9
4.3.3 Acceptance and availability of height classes
9
4.3.4 Impact modes, impact mode intensity and plant part utilization
10
4.4
Impact modes
10
Impact mode intensity
11
Plant part utilization
12
Results
13
4.4.1 Woody plant species – availability and utilization
13
4.4.2 Utilization of trees and shrubs
17
4.4.3 Acceptance and availability of height classes
18
4.4.4 Impact modes, impact mode intensity and plant part utilization
20
Impact modes
20
v
Impact mode intensity
23
Plant part utilization
25
4.5
Discussion
28
4.6
Summarizing conclusion and implications
34
4.7
References
37
Chapter 5
5.1
Sex-related distinctions in the dry season feeding ecology of the
African elephant, Loxodonta africana
Introduction
1
5.1.1 Objectives
4
5.2
Methods
5
5.3
Data analysis
5
5.3.1 Woody plant species – availability and utilization
5
5.3.2 Utilization of trees and shrubs
6
5.3.3 Acceptance and availability of height classes
6
5.3.4 Impact modes, impact mode intensity and plant part utilization
7
5.4
Impact modes
7
Impact mode intensity
7
Plant part utilization
8
Results
8
5.4.1 Woody plant species – availability and utilization
8
5.4.2 Utilization of trees and shrubs
12
5.4.3 Acceptance and availability of height classes
13
5.4.4 Impact modes, impact mode intensity and plant part utilization
15
Impact modes
15
Impact mode intensity
16
Plant part utilization
17
5.5
Discussion
19
5.6
Summarizing conclusion and implications
25
5.7
References
26
Chapter 6
6.1
Conclusion, Limitations, Recommendations
1
6.2
References
6
vi
Appendix A
1
Appendix B
2
Appendix C
4
Appendix D
10
LIST OF FIGURES
Chapter 2
Figure 2.1
Map of the study area adjacent to the west of the Kruger National Park,
showing the APNR comprising KPNR, UPNR, TPNR and specifically
Balule PNR, which houses the two study sites OWNR and JPNR.
2
Relative proportions of available shrubs and trees
- study sites compared.
7
Relative proportions of single-stemmed and multi-stemmed shrubs
within each study site - study sites compared.
8
Relative proportions of single-stemmed and multi-stemmed trees
within each study site - study sites compared.
8
Distribution of height classes across each study site - study sites
compared.
9
Rarefaction curve for OWNR (a) and JPNR (b), based on the overall
(pooled) cumulative no. of species.
11
Frequency distribution of Sclerocarya birrea across age classes - study
sites compared.
12
Chapter 3
Figure 3.1
Figure 3.2
Figure 3.3
Figure 3.4
Figure 3.5
Figure 3.6
Figure 3.7
Frequency distribution of Acacia nigrescens across ‘basal circumference
classes’ - study sites compared.
13
Figure 3.8
Frequency distribution of ‘basal circumference classes’ - comparing
Sclerocarya birrea and Acacia nigrescens populations
- pooled across study sites.
14
Acceptance and availability indices for woody species that were
available at ten or more food plots on OWNR (a) and JPNR (b).
14
Acceptance and availability indices for woody species that were
available at ten or more food plots - pooled across study sites.
16
Chapter 4
Figure 4.1
Figure 4.2
vii
Figure 4.3
Relative dietary contributions of the eight woody plant species that were
commonly utilized by elephants during the late dry season, study sites
combined. The value in brackets shows the number of utilized individuals
within each species.
16
Figure 4.4
Proportional utilization and negligence of trees and shrubs - pooled across
study sites, for ‘new’ impact data (a) and for ‘old’ impact data (b),
respectively.
17
Figure 4.5
Distribution of impact frequencies, illustrating the proportion of utilized
trees (a),(c) and shrubs (b),(d) within different height classes in relation
to their 100% availability, for ‘old’ (a),(b) impact data and for ‘new’
(c),(d) impact data - pooled across study sites.
19
Figure 4.6
Frequency distribution of impact mode events per height class for OWNR
‘new’ (a), JPNR ‘new’ (b), OWNR ‘old’ (c), JPNR ‘old’ (d).
21
Figure 4.7
Proportions of impact mode events and non-impact events for ‘new’ (a)
and ‘old’ (b) impact data with the frequency values in brackets - pooled
across study sites.
22
Degrees of intensity and their proportional share for ‘new’ (a) and
accumulative ‘old’ (b) impact - pooled across study sites.
24
Figure 4.8
Figure 4.9
Proportional share of plant part utilization within the late dry season diet
- study sites pooled, with the frequency values in brackets.
26
Figure 4.10
Frequency distribution of utilized plant parts
across height classes 1-10.
27
Acceptance and availability indices for woody species that were
available at ten or more food plots used by bull groups (a) and
family units (b).
9
Relative dietary contributions of the eight woody plant species that
were commonly utilized by bull groups (a) and family units (b) during
the late dry season - study sites combined.
11
Proportional utilization and negligence of shrubs (a) and trees (b)
- bull groups versus family units.
12
Relationship between acceptance values for height classes 1 to 10 of
bull groups and family units.
13
Proportional share of plant part utilization within the late dry season
diet of bull groups (a) and family units (b),
with the frequency values in brackets.
17
Frequency distribution of utilized plant parts across height classes
by bull groups (a) and family units (b).
18
Chapter 5
Figure 5.1
Figure 5.2
Figure 5.3
Figure 5.4
Figure 5.5
Figure 5.6
viii
Appendix
Figure A 1.1 OWNR study site - elephant route and transect locations
1
Figure A 1.2 JPNR study site - elephant route and transect locations
1
Figure C.9
Proportional share of plant part utilization within the total diet of
elephant for OWNR (a) and JPNR (b), with the frequency values in
brackets.
9
LIST OF TABLES
Chapter 3
Table 3.1
Biodiversity indices - study sites compared.
10
Percentage share of impact mode events for ‘new’ (a) impact data and
‘old’ (b) impact data.
20
Degrees of intensity and their proportional share for ‘new’ and
accumulative ‘old’ impact, study sites compared.
23
Frequency distribution of impact modes and their intensity that was
recorded on the two vegetation transects on OWNR during the late dry
season (August, September, October) over a period of 3 years.
25
Chapter 4
Table 4.1
Table 4.2
Table 4.3
Table 4.4
Proportional share of plant part utilization within the late dry season,
comparing values between ‘study sites pooled’ with ‘OWNR’ and ‘JPNR’.
26
Chapter 5
Table 5.1
Table 5.2
Table 5.3
Frequency distribution of available and utilized woody plants across
height classes - bull groups versus family units.
14
Distribution of impact mode frequencies and their proportional share
- units compared.
15
Impact mode intensity - Frequency of occurrence and proportional
share of ‘heavy’ (orange) and ‘light to moderate’ (green) impact
- social units compared.
16
ix
Appendix
Table B.1
Woody plant species sampled during the late dry season on both study
sites; if only recorded on one site, this is indicated by the name of the site.
2
Table B.2
Age estimation for the marula tree (Sclerocarya birrea) populations on
OWNR and JPNR- basal circumference measurements in cm and age
in years (from regression equation adopted from Haig (1999)).
3
Table C.1
Availability and Acceptance indices for the eight core species, study sites
compared.
4
Table C.2
Height class acceptance ratios for ‘new’ (a) (paired t-test, P=0.093;
P=0.312) and ‘old’ (b) (paired t-test, P=0.255; P=0.540) impact
respectively - study sites compared.
4
Acceptance ratios for ‘new’ (Wilcoxon signed-rank test, P=0.889)
and ‘old’ (paired t-test, P=<0.001) impact data
- pooled across study sites.
5
Frequency distribution of impact modes across height classes for ‘new’
impact (a) and ‘old’ impact (b) - study sites compared.
6
Sum of impact mode frequencies for ‘new’ (a) impact data
(chi-square test, χ2 = 4.252, df = 4, P = 0.373) and for ‘old’ (b) impact
data (chi-square test, χ2 = 2.777, df = 4, P = 0.596)
- study sites compared.
7
Frequency distribution of impact modes across height classes
- study sites pooled, ‘new’ and ‘old’ impact compared.
7
Sum of impact mode frequencies - study sites pooled, ‘new’ and ‘old
impact compared (chi-square test, χ2 = 61.752, df = 4, P = <0.001).
8
Frequency distribution for ‘degrees of intensity’ and their
proportional share for accumulative ‘old’ (a) and ‘new’ (b) impact
- study sites compared and pooled.
8
Frequency distribution of plant part utilization as share of total diet
- study sites compared (chi-square test, χ2 = 2.208, df = 4, P = 0.698).
9
Availability (Pearson correlation, P = <0.001) and Acceptance (Pearson
correlation, P = 0.32 with Sbir, P = 0.007 without Sbir) indices for the
eight core species - bull groups versus family units.
10
Height class acceptance values - bull groups versus family units
(Pearson correlation, rs = 0.827, P = 0.003).
10
Table D.3
Rainfall data 1985-2013, OWNR, provided by G.Reinstorf
11
Table D.4
Rainfall data 1968-2013, JPNR, provided by G.Thomson
12
Table C.3
Table C.4
Table C.5
Table C.6
Table C.7
Table C.8
Table C.9
Table D.1
Table D.2
x
Chapter 1
Chapter 1
The African elephant (Loxodonta africana), ‘landscape architect’ and
‘ecosystem engineer’ –
A general introduction to its feeding ecology and habitat use and the
potential consequences for ecosystem and biodiversity
1.1 Introduction
The African elephant (Loxodonta africana (Blumenbach)), classified as ‘ecosystem engineer’
(Haynes, 2012; Vanak et al., 2010; Wright and Jones, 2006), i.e. functioning as an agent of
habitat modification, was recorded to have considerable impact on vegetation community and
habitat structure of the woody vegetation layer in the arid savanna biome (Kerley and
Landman, 2006). In fact, Thomson (1975) and Laws (1970) considered the African elephant
to be the preeminent agent of habitat change. Several studies have revealed that elephants
seem to be one of the prime driving forces in the conversion of species-rich woodlands to
shrubland and species-poor grassland (Caughley, 1976; Cumming et al., 1997; Shannon et
al., 2008; Stokke and Du Toit, 2000). Cumming et al. (1997) argued that woodlands mostly
converted to shrubland where elephant density exceeded 0.5 km2 and that woodland damage
would still occur with densities of less than 0.2 km2, potentially resulting in a loss of
vulnerable tree species. Such changes in vegetation composition are the consequences of the
elephant’s foraging behaviour, potentially leading to habitat degradation and therefore
controlling the resource availability of other species (Cumming et al., 1997; de Beer et al.,
2006).
In order to mitigate these impacts, effective management principles have to be established
and thus a sound understanding of the local vegetation structure and composition on the one
hand and of the species’ feeding behaviour, biology and distribution patterns on the other
hand, is crucial. Although many studies concerning elephant ecosystem interactions have
already been conducted, the management of elephants in areas that have just recently been
exposed to elephants and which are therefore experiencing the ‘initial phase’ of elephant
impact remains a major challenge and requires continuous assessment.
The majority of discussions on the ‘elephant problem’ are restrained by political, ethical and
economic implications and most often end with the heavily burdened and ultimate question as
to whether culling is appropriate or not (Guldemond et al., 2008; Skarpe et al., 2004). The
1
Chapter 1
practice of culling is not only inconsistent with conservation (Dublin et al., 1990), but is also
highly controversial and can disturb natural population dynamics (Van Aarde et al., 1999).
Impacts on structure and vegetation community composition of the woody vegetation layer
vary from reduction in recruitment rates by feeding on seedlings (Campbell et al., 1996;
Dublin, 1995; Jachmann and Bell, 1985) to removal of mature trees (Dublin, 1995).
Elephants were documented to be selective feeders (Owen-Smith, 1988). The preference for
distinct tree and shrub species could potentially make these more vulnerable for local
extinction, therefore driving the change of vegetation community and structure and hence
threatening the indigenous biodiversity.
Gadd (2002) argued that the consumption of woody vegetation is a complex function which
depends on vegetation composition, tree species distribution, tree size, feeding preferences
and habits. However, it is necessary to acknowledge that the elephant can neither be judged
as the only driving force responsible for landscaping the ecosystem and hence shaping
architecture and structure of woody plant communities (Ben-Shahar, 1996, Owen-Smith et
al., 2006, Shannon et al., 2008, Van de Koppel and Prins, 1998), nor should the idea be
promoted that the influence elephants exert on their environment is solely one of severe and
negative impact. Various other ecosystem drivers, such as fire, general herbivory, rainfall
patterns or texture and nutritional quality of the soil, also affect the dynamics of the woody
vegetation layer (Scholes, 1997). Furthermore, the landscaping effect on vegetation structure
and community can lead to habitat heterogeneity, which can in turn be very beneficial to the
ecosystem. Elephants contribute to biomass recycling as 56% of the consumed forage passes
through their digestive system without being assimilated and hence remains undigested
(Clauss et al., 2007; Davis, 2007). The toppling of mature trees and the foraging of branch
ends and leaves allow for compensatory regeneration (Fornara and Du Toit, 2007; Makhabu
et al., 2006). Therefore, nutrient cycling and primary production (N → crude proteins) occur
at a greater rate in the presence of elephants (Scholes et al., 2008). Additionally, elephants
promote seed dispersal and germination (Cochrane, 2003), thus facilitating regeneration.
Midgley et al. (2012) supported the idea of enhanced germination of marula (Sclerocarya
birrea) fruits, which pass through the elephant’s digestive system, but proposed that, rather
than resulting from the digestive acid treatment, rapid germination is a consequence of
mastication (‘large bite-force hypothesis’). With the generation of ‘browsing lawns’,
elephants may, moreover, promote foraging opportunities for other herbivores, as suggested
by Makhabu et al. (2006) and Kohi et al.(2011).
2
Chapter 1
Further long-term research needs to be done in order to gain more detailed insight into how
the African elephant is affecting biodiversity and species abundance at local and wider spatial
levels and to what extent this impact is predominantly of a negative nature or whether these
animals increase structural habitat heterogeneity, promote biodiversity and therefore enhance
ecosystem complexity (Caughley, 1976; Dublin et al., 1990; Hastings et al., 2007; Pringle,
2008; Wright and Jones, 2006).
As a keystone species (Kerley and Landman, 2006; Makhabu et al., 2006), the African
elephant has an impact on its ecosystem which is comparatively large. Thus, the effects that
will be achieved by manipulating population numbers to either minimum or maximum will
most likely cascade through the entire ecosystem, influencing a large proportion of flora and
fauna (Chafota, 1998; Western, 1989). Therefore, a ‘single species’ approach, as an elephant
management tool, needs to be considered very carefully (Smit et al., 2007a).
The paradox of over-abundance in some areas and the threat of extinction in others is the
source of the complex ‘elephant debate’, which has never been as clear and apparent as in
present times (Loarie et al., 2009; Owen-Smith et al., 2006). In many parts of Africa,
elephants roam freely outside of game refuges. These animals not only constantly suffer from
habitat degradation due to the ever growing spread of human settlements, but are still
threatened of falling victim to poaching activities (Osborn et al., 2003). In contrast, the
distribution of elephants in Southern Africa is largely restricted to fenced-off conservation
areas like the Kruger National Park. The lack of dispersal options in such confined areas and
the high resource availability contribute to the steady increase in population size (Loarie et
al., 2009; Smit et al., 2007a; Trollope et al., 1998). The population expansion within these
bounded areas has caused growing concern among conservationists and reserve managers,
with special regard to the impact on vulnerable vegetation, in particular to specific woody
plant species (Guldemond and Van Aarde, 2008; Kerley and Landman, 2006; Young et al.,
2009).
Van Aarde and Jackson (2007) described the root of the ‘elephant problem’ as the local
concentration of high numbers and criticized the fact that most management principles dealt
with the symptoms (i.e. the numbers) but neglected treating its cause. When given the
opportunity, elephants do disperse and hence high local densities will consequently
decentralize, allowing metapopulation dynamics to become the driving force of elephant
populations in Southern Africa (Chafota, 1998; Thomas et al., 2011; Van Aarde and Jackson,
2007). Wittemyer et al. (2007) argued that elephants were sensitive to intraspecific
competition and hence avoided areas where densities are high in order to optimize foraging
3
Chapter 1
opportunities (Young et al., 2009). This would imply that dispersal allows for a reduction in
local elephant densities and therefore brings about a decrease in the intense concentration of
local impact, thereby allowing for regeneration windows, while the utilization of different
habitats will spread heterogeneously over a larger spatial scale, however with potentially
lower intensity.
This idea of dispersal as an appropriate elephant management tool is given expression by the
endeavour to drop fences, where feasible, within South Africa and beyond. In recent years
national and international efforts, driven by the Peace Parks Foundation, have made the idea
possible of establishing Transfrontier Conservation Areas, which stretch beyond boundaries
(Western, 2003). Whether this attempt will promote spatial and temporal heterogeneity of
distribution patterns and whether it will moderate the reported intensity of impact, ultimately
solving the problem, remains unanswered for now and will only be revealed through future
observations.
However, pieces of land that are joining the large network of conservation areas can function
as ‘dispersal sinks’ (Lidicker, 1975; Owen-Smith, 1983; Van Aarde and Jackson, 2007). This
may result in a potential increase in elephant numbers, which is assumed to be accompanied
by an intensification of impact on the vegetation structure and composition, and may
therefore be the cause of considerable concern for landowners and Nature Park managers.
Such a scenario is presently the case in the two Nature Reserves Olifants West Nature
Reserve [hereafter, OWNR] and Jejane Private Nature Reserve [hereafter, JPNR], both of
which are part of the Balule Nature Reserve [hereafter, BNR], which, in turn, is a member of
the Associated Private Nature Reserves [hereafter, APNR] (Olifants West, 2013). In
agreement with Spencer (2010), who reported an instant rise in immigration rates from the
neighbouring reserves once the last fences between the Greater Kruger National Park and
Balule were dropped in 2004/5, Peel (2012) recorded a decrease (0.7 elephants/km2) in BNR
elephant density after the pronounced initial increase (1.3 elephants/km2) and presented
census results that presently show slightly fluctuating but similar numbers of elephants to
those of 2009 (0.67 elephants/km2). A similar pattern of a high elephant immigration rate was
recently reported for the Mohlabetsi South Nature Reserve [hereafter, MSNR], housing
JPNR, which officially joined the BNR by removing the fences in March 2013 (Thomson,
2013).
Since the fence removal in the two study sites and hence the return of elephant activity after
more than several decades, concern is growing about an alteration of the overall vegetation
structure in general and a potential decline in large trees, in particular the loss of certain
4
Chapter 1
species which are of exceptional value to landowners and managers. Among these species is
the marula tree (Sclerocarya birrea), which is considered to be a large tree keystone species
(Helm and Witkowski, 2012). Marula trees, when pushed over, have the ability to resprout
epicormically as well as from the base (Gadd, 2002) and to regrow when bark-stripped
(Coetzee et al., 1979), and are therefore described as being able to withstand higher levels of
disturbance. However, Helm et al. (2009) found an increased impact by elephants on this tree
species and annual mortality rates of up to 7.8% on certain mature marula size classes
between 2001 and 2008. Furthermore, it was observed that the level of recruitment into the
seedling size stayed below the level of mortality and that sapling growth into heights greater
than 2 m was absent (Helm et al., 2009; Helm and Witkowski, 2012). Moreover, Helm et al.
(2009) described how the level of impact caused by elephants on, and the utilization of, the
surveyed marula individuals within the height class of 5m to 8m increased threefold between
2001 and 2008. However, because the discussion about the underlying ecological drivers is
extremely complex as, for example, shown by Walker et al.’s (1986) theory, which explains
the low recruitment rate of marula trees into mature height classes with the episodic nature of
recruitment events that are limited by rainfall patterns, or by Lewis’ (1987) suggestion, which
stressed the negative effect of impala (Aepyceros melampus) browsing on marula seedlings,
species specific monitoring programmes need to be considered.
Recordings (Spencer et al., 2011) from 2008 onward illustrated the correlation between
rainfall and elephant herd demographics on OWNR. The trend revealed two visitation peaks.
The first annual influx takes place just after the first summer rains while the second rise in
elephant sightings is observed during the dry winter months. This trend indicates that
elephants occupy the area when the vegetation is highly nutrient rich after the first rainfall,
but then disperse during the main part of the wet season, as foraging opportunities (grass and
foliage) are overall abundant (Shannon et al., 2013). Once overall grass quantities and
quality are considerably limited and most of the vegetation dormant, sightings again become
more frequent. Whether this second wave of influx results from the fact that the APNR is
saturated with artificial waterholes, the mean distance between artificial water sources being
2km (Stalmans et al., 2002), and is located close to the perennial Olifants River, or whether it
is a consequence of the different vegetation communities (western granites versus eastern
basalt habitats), as edaphic factors in the Greater Kruger play an essential role in regulating
high quality nutrient patches (Naiman et al., 2003) and therefore potentially influence
browsing patch selection and utilization, must be further investigated, a necessity that is
beyond the scope of this study.
5
Chapter 1
These temporal distribution patterns imply that, in the search for crude proteins, the
utilization of the woody vegetation layer (trees and shrubs) significantly increases during the
dry season (Dublin, 1995) and thereby supports the observation that elephants clearly show a
preference for the fresh grasses after the first rainfall, when the herbaceous layer is abundant
and rich in proteins (Dublin, 1995; Sinclair, 1975).
As mixed but selective feeders, elephants benefit from the ability to adapt their diet according
to season (Codron et al., 2011; Osborn, 2004; Shannon et al., 2013), thereby optimizing their
nutritional intake. Elephants are hindgut fermenters, a trait which allows them to feed on
plants that contain a great proportion of fibre without slowing down the digestive passage to
an extent that energetic intake requirements would not be met (Clauss et al., 2003; Janis,
1976). Hence, they are able to process large amounts of potentially low quality forage in
order to meet energetic and nutritional requirements (Codron et al., 2006).
The African elephant is water-dependent and therefore generally found in close proximity to
rivers or artificial water sources. Water has been described as the dominant determinant of its
distribution patterns (Chamaille-Jammes et al., 2007; De Beer et al., 2006), implying that the
manipulation of surface water resources has important consequences for the movements of
elephants and hence influences the way the elephant forages at spatial levels (De Beer et al.,
2006). During the dry season elephants are more restricted by water resource availability and
consequently cover smaller areas, while family units with offspring generally remain in
closer proximity to water than socially independent bulls. In contrast, elephants roam over
huge areas during the wet season when distribution patterns are more relaxed as ephemeral
pools become available for drinking purposes (Chamaille-Jammes et al., 2007; Leggett et al.
2003, 2006a/b; Smit et al., 2007a,b; Stokke and Du Toit, 2002). However, such natural
distribution patterns are likely to become skewed in areas where artificial surface water
sources are ubiquitous (Grainger et al., 2005). The increase in home range size during the wet
season is crucial in order to allow for windows of vegetation regeneration, implying that a
decrease in seasonal home ranges result in impact intensity, while leaving the vegetation little
chance for regeneration (Van Aarde et al., 2006). Thomas et al. (2011), for example, found
that elephants in the Sabi Sand Reserve stayed closer to water regardless of season, i.e.
showing a decrease in the wet season home range size, than if they were naturally distributed.
The distinct concentration of elephants around these core areas results in the generation of a
‘radial pattern of attenuating impact’ (Landman et al., 2012), termed as the ‘piosphere’ effect.
These gradients around the concentrator (water) are expressed by intensified impact on
vegetation structure and community close to the water source, decreasing with distance
6
Chapter 1
(Chamaille-Jammes et al., 2007). Landman et al. (2012) argued that this alteration of
vegetation can lead to the gradual extinction of less tolerant tree species that regenerate
poorly. Loarie et al. (2009) suggested that the provision of artificial water sources allows
elephants to overexploit vegetation in areas they would not have visited during the dry season
under natural circumstances, as water would not have been available. Smit et al. (2007a)
emphasized that the use of surface water manipulation as an effective management principle
has to be applied in an area- and population-specific context. Additionally, they argued that a
‘single species’ approach may have ramifying consequences for less mobile water-dependent
species. However, the manipulation of waterhole distribution could be a promising and nonintrusive tool to approach the so-called ‘elephant problem’ (Chamaille-Jammes et al., 2007a)
with regard to the impact on woody vegetation.
Fences have a major influence on elephant movement and behaviour and consequently
determine the structure and intensity of habitat utilization (Loarie et al., 2009). The ‘edge
effect’ fences potentially impose on the behaviour of elephants is, according to Vanak et al.
(2010), poorly understood. It was found that elephants either develop an aversive behavioural
response, hence avoiding areas in proximity to fences and transferring the edge effect to
central areas, or ‘bunch up’ close to fences, regularly revisiting these areas and consequently
over-exploiting the vegetation near fences (Loarie et al., 2009; Vanak et al., 2010). This
implies that management strategies may need to consider a potential increase in browsing
pressure caused by fences in certain areas, depending on the elephants’ reaction, which could
be either avoidance of or bunching up along fences.
Although it was assumed that the degree of impact can be related to the proximity of water,
with an increase in damage near water sources, and that impact on the vegetation in areas
near fences varies from the impact on vegetation away from fences, a detailed investigation
of these variables was beyond the scope of this study. However, while considering the
various details which are potentially involved in the interaction of elephant and ecosystem,
this study examined and quantified the impact of the African Elephant on the woody
vegetation layer of the semi-arid savanna system during the late dry season.
7
Chapter 1
1.2 Objectives and outcomes
The aim of this study was to achieve a better comprehension of the current vegetation state in
terms of woody vegetation structure and community (Chapter 3), of the late dry season diet
and utilization of woody species by elephants in general (Chapter 4) and of sex-related
differences in elephant diets (Chapter 5) on the two study sites.
The results will provide information on the feeding ecology of elephants during the late dry
season period, while giving insight into feeding preferences and the degree of impact
elephants have on the woody vegetation layer over the landscape, when seasonal competition
for food resources are potentially at their highest. Recordings furthermore form a baseline
study of the local vegetation community and structure which, if continued over time, could
provide information on the vegetation integrity, heterogeneity and system change over the
longer term. The study will therefore contribute towards the knowledge base on which
management decisions are taken within the Private Nature Reserves.
This knowledge will advise judgements on whether the reserve could support a potentially
increasing number of elephants in the future. Results could furthermore be understood as a
guideline for management activities such as bush-clearing by the identification of buffer
species.
The following objectives were set in consultation with my supervisors:
1. To provide a benchmark survey of the site specific vegetation structure (shrubs versus
trees, height class distribution) and community (species) which will furnish baseline records
for future results in the intended long-term monitoring programme.
2. To determine the potential alteration in the vegetation structure and community of the two
study sites.
3. To achieve an understanding of the late dry season diet of elephants in terms of:
3.1 The acceptance of shrubs, trees and height classes in general and of distinct
species in particular, thereby quantifying potential preferences.
3.2 The utilization of different impact modes.
3.3 The proportional utilization of different plant parts in a seasonal context.
4. To provide an assessment of the potential influence that elephants exert on the woody
vegetation layer with the aim of assisting managers to make decisions.
8
Chapter 1
5. To determine the difference in choice of woody species and plant parts between different
herd demographics (bull groups as opposed to family units), thereby identifying potential
sex-related differences in feeding patterns.
1.3 Thesis structure
Chapter 1 provides a general introduction to the ecosystem interaction, habitat use and
feeding ecology of the African elephant and its potential consequences for the woody
vegetation layer, thereby explaining the problematic nature which gives rise to this study.
Chapter 2 introduces the study area and provides an historical background of elephant
presence within the APNR, while additionally describing the survey techniques that were
used for the data collection during this study. Chapter 3 analyses the current state of the
vegetation structure and composition within the two reserves with a separate focus on the
populations of two tree ‘species of concern’ (Sclerocarya birrea and Acacia nigrescens).
Chapter 4 investigates the general feeding habits of elephants and their consequences for the
woody vegetation layer, while Chapter 5 evaluates potential sex-related differences in the
utilization of the woody vegetation. Chapter 6 discusses the limitations of this study and
outlines the resultant recommendations for future monitoring programmes, while providing
considerations on potential management options.
The fact that each chapter is introduced separately might lead to some overlap between the
general literature review and the individual introductions. Methods used for the statistical
data analysis are defined separately in each chapter.
9
Chapter 1
1.4 References
Bell, R.H.V. (1971). A grazing ecosystem in the Serengeti Scientific American. 225, pp. 8693.
Ben-Shahar, R. (1996). Do elephants over-utilize mopane woodlands in northern Botswana?
Journal of Tropical Ecology. 12, pp. 505-515.
Boundja, R.P., Midgley, J.J. (2009). Patterns of elephant impact on woody plants in the
Hluhluwe-Imfolozi Park, Kwazulu Natal, South Africa African Journal of Ecology. 48,
pp. 206-214.
Campbell, B.M., Butler, J.R.A.,Mapaure, I., Vermeulen, S.J., Mashove, P. (1996). Elephant
damage and safari hunting in Pterocarpus angolensis woodland in northwestern
Matabeleland, Zimbabwe African Journal of Ecology. 34, pp. 380-388.
Caughley, G. (1976). The elephant problem: an alternative hypothesis East African Wildlife
Journal. 14, pp. 265-283.
Chafota, J. (1998). Effects of changes in elephant densities on the environment and other
species – How much do we know? Cooperative Regional Wildlife Management in
Southern Africa.
Charif, R.A., Ramey, R.R., Langbauer, W.R., Payne, K.B., Martin, R.B., Brown, L.M.,
(2005). Spatial relationships and matrilineal kinship in African savanna elephant
(Loxodonta africana) clans Behavioural Ecologyand Sociobiology. 57, pp. 327-338.
Chamaille-Jammes, S., Valeix, M., Fritz, H. (2007). Managing heterogeneity in elephant
distribution: interactions between elephant population density and surface-water
availability Journal of Applied Ecology. 44, pp. 625-633.
Chamaille-Jammes, S., Valeix, M., Fritz, H. (2007). Elephant management: why can’t we
throw out the babies with the artificial bathwater? Diversity and Distributions. 13, pp.
663-665.
Clauss, M., Streich, W.J., Schwarm, A., Ortmann, S., Hummel, J. (2007). The relationship of
food intake and ingesta passage predicts feeding ecology in two different
megaherbivore groups Oikos. 116, pp. 209-216.
Clauss, M., Frey, R., Kiefer, B., Lechner-Doll, M., Loehlein, W., Polster, C., Rossner, G.E.,
Streich, W.J. (2003). The maximum attainable body size of herbivorous mammals:
morphophysiological constraints on foregut and adaptations on hindgut fermenters
Oecologia. 136, pp. 14-27.
10
Chapter 1
Codron, J., Lee-Thorp, J.A., Sponheimer, M., Codron, D., Grant, R.C., de Ruiter, D.J. (2006).
Elephant (Loxodonta africana) diets in Kruger National Park, South Africa: spatial and
landscape differences Journal of Mammalogy. 87 (1), pp. 27-34.
Cochrane, E.P. (2003). The need to be eaten: Balanites wilsoniana with and without elephant
seed-dispersal Journal of Tropical Ecology. 19, pp. 579-589.
Codron, J., Codron, D., Lee-Thorp, J.A., Sponheimer, M., Kirkman, K., Duffy, K., Sealy, J.
(2011). Landscape-scale feeding patterns of the African elephant inferred from carbon
isotope analysis of feces Oecologia. 165, pp. 89-99.
Coetzee, B.J., Engelbrecht, A.H., Joubert, S.C.J., Retief, P.F. (1979). Elephant impact on
Scleroocarya caffra tress in Acacia nigrescens tropical plains thornveld of the Kruger
National Park Koedoe. 22, pp. 39-66.
Cumming, D.H.M., Fenton, M.B., Rautenbach, I.L., Taylor, R.D., Cumming, G.S.,
Cumming, M.S., Dunlop, J.M., Ford, G.S., Hovorka, M.D., Johnston, D.S., Kalcounis,
M.C., Mahlanga, Z., Portfors,, C.V. (1997). Elephants, Woodlands and Biodiversity in
South Africa South African Journal of Science. 93, pp. 231-236.
Davis, S. (2007). Endozoochory in Subtropical Thicket: comparing effects of species with
different digestive systems on seed fate. MSc thesis, Nelson Mandela University, South
Africa.
De Beer, Y., Kilian, W., Versveld, W., Van Aarde, R.J. (2006). Elephants and low rainfall
alter woody vegetation in Etosha National Park Namibia Journal of Arid Environments.
64, pp. 412-421.
Demment, M.W. (1983). Feeding ecology and the evolution of body size of baboons African
Journal of Ecology. 21, pp. 219-233.
Demment, M.W., Van Soest, P.J. (1985). A nutritional explanation for body-size patterns of
ruminant and nonruminant herbivores The American Naturalist. 125, pp. 641-672.
Dublin, H.T., Sinclair, A.R.E., McGlade, J. (1990). Elephants and fire as causes of multiple
stable states in the Serengeti-Mara woodlands Journal of Animal Ecology. 59, pp. 11471164.
Dublin, H.T. (1995). Vegetation Dynamics in the Serengeti-Mara ecosystem: the role of
elephants, fire and other factors, p.71-90. IN: Sinclair, A.R.E., Arcese, P. (eds.)
Serengeti II: Dynamics, Management, and Conservation of an Ecosystem. University of
Chicago Press, Chicago.
Fornara, D.A., Du Toit, J.T. (2007). Browsing lawns? Responses of Acacia nigrescens to
ungulate browsing in an African savanna Ecology. 88, pp. 200-209.
11
Chapter 1
Gadd, M. (1997). Factors influencing the impact of elephants on woody vegetation in private
protected areas in South Africa’s Lowveld. PhD Thesis, University of Witwatersrand,
Johannesburg.
Gadd, M.E. 2002. The impact of elephants on the marula tree Sclerocarya birrea African
Journal of Ecology. 40, pp. 328–336.
Grainger, M., Van Aarde, R., Whyte, I. (2005). Landscape heterogeneity and the use of space
by elephants in Kruger National Park, South Africa African Journal of Ecology. 43, pp.
369-375.
Greyling, M.D. (2004). Sex and age related distinctions in the feeding ecology of the African
elephant, Loxodonta africana. PhD Thesis, Faculty of Science, University of the
Witwatersrand, Johannesburg.
Guldemond, R., Van Aarde, R.J., (2008). A meta-analysis of the impact of African elephants
on savannah vegetation Journal of Wildlife Management. 72 (4), pp. 892-899.
Hastings, A., Byers, J.E., Crooks, J.A., Cuddington, K., Jones, C.G., Lambrinos, J.G., Talley,
T.S., Wilson, W.G. (2007). Ecosystem engineering in space and time Ecology Letters.
10, pp. 153-164.
Haynes, G. (2012). Elephants (and extinct relatives) as earth-movers and ecosystem engineers
Geomorphology. 157-158, pp. 99-107.
Helm, C.V., Witkowski, E.T.F., Kruger, L., Hofmeyer, M., Owen-Smith, N. (2009).
Mortality and utilization of Sclerocarya birrea subs. Caffra between 2001 and 2008 in
the Kruger National Park South Africa South African Journal of Botany. 75, pp. 475484.
Helm, C.V., Witkowski, E.T.F. (2012). Continuing decline of a keystone tree species in the
Kruger National Park South Africa African Journal of Ecology. 51, pp. 270-279.
Jachmann, H., Bell, R.H.V. (1985). Utilization by elephants of the Brachystegia woodlands in
the Kasungu National Park, Malawi African Journal of Ecology. 23, pp. 235-253.
Janis, C. (1976). The evolutionary strategy of the equidae and the origin of rumen and cecal
digestion Evolution. 30, pp. 757-774.
Jarman, P.J. (1974). The social organization of antelope in relation to their ecology
Behaviour. 48, pp. 215-267.
Jejane (2013). History – timeline for Jejane Private Nature Reserve. Available from:
www.jejane.co.zza/tags/history [accessed 13th Februaury 2013].
Kerley, G.I.H., Landman, M. (2006).The impacts of elephants on biodiversity in the Eastern
Cape subtropical thickets South African Journal of Science. 102, pp. 395-402.
12
Chapter 1
Kohi, E.M., de Boer, W.F., Peel, M.J.S., Slotow, R., Van der Waal, C., Heitkoenig, I.M.A.,
Skidmore, A., Prins, H.H.T. (2011). African elephants LoxodontaAfricana browse
heterogeneity in African savanna Biotropica. 43 (6), pp. 711-721.
Landman, M., Schoeman, D.S., Hall-Martin, A.J., Kerley, G.I.H. (2012). Understanding
long-term variations in an elephant piosphere effect to manage impacts Plos One. 7 (9),
pp. 1-11.
Laws, R.M. (1970). Elephants as agents of habitat and landscape change in East Africa
Oikos. 21, pp. 1-15.
Lee, P.C., Moss, C.J. (1995). Statural growth in known-age African elephants (Loxodonta
africana) Journal of Zoology, London. 236, pp. 29-41.
Leggett, K.E.A. (2006a). Effect of artificial water points on the movements and behaviour of
desert-dwelling elephants of north-western Namibia Pachyderm. 40, pp. 40-51.
Leggett, K.E.A. (2006b). Home range and seasonal movement of elephants in the Kunene
region, northwestern Namibia African Zoology. 41, pp. 17-36.
Leggett, K.E.A., Fennessey, J., Schneider, S. (2003). Seasonal distributions and social
dynamics of elephants in the Hoanib river catchment, northwestern Namibia African
Zoology. 38, pp. 305-316.
Lewis, D.M. (1987). Fruiting patterns, seed germination and distribution of Sclerocarya
caffra in an elephant inhabited woodland Biotropica. 19, pp. 50-56.
Lidicker, W.Z. ,Jr. (1975). The role of dispersal in the demography of small mammals, In:
Golley, F.B., Petrusewicz, K., Ryzkowski, L., (eds.) Small mammals: their productivity
and population dynamics. Cambridge University Press, Cambridge, Massachusetts, pp.
103-128.
Loarie, S.R., Van Aarde, R.J., Pimm, S.T. (2009). Fences and artificial water affect African
savanna elephant movement patterns Biological Conservation. 142, pp. 386-398.
Makhabu, S., Skarpe, C., Hytteborn, H. (2006). Elephant impact on shoot distribution on
trees and on rebrowsing by smaller browsers Acta Oecologica. 30, pp. 136-146.
McComb, K., Moss, C., Durant, S.M., Baker, L., Syialel, S. (2001). Social knowledge in
African Elephants Science. 292, pp. 491-494.
Midgley, J.J., Gallaher, K., Kruger, L.M. (2012). The role of the elephant (Loxodonta
africana) and the tree squirrel (Paraxerus cepapi) in marula (Sclerocarya birrea) seed
predation, dispersal and germination Journal of Tropical Ecology. 28 (2), pp. 227-231.
Olifants West (2013). OWNR – Introduction and Background. Available from:
www.olifantswest.co.za [accessed 13th February 2013]
13
Chapter 1
Osborn, F.V. (2004). Seasonal variation of feeding patterns by crop-raiding elephants in
Zimbabwe African Journal of Ecology. 42, 322-327.
Owen-Smith, R.N. (1983). Dispersal and dynamics of large herbivores in enclosed areas:
implication for management In: Owen-Smith, R.N., (ed.) Management of Large
Mammals in African Conservation Areas. Haum Educational Publishers, Pretoria,
South Africa, pp. 127-144.
Olifants West (2013). OWNR – Introduction and Background. Available from:
www.olifantswest.co.za [accessed 15th February 2013]
Owen-Smith, R.N. (1988). Megaherbivores: the influence of very large body size on ecology.
Cambridge, Cambridge University Press.
Owen-Smith, R.N., Kerley, G.I.H., Page, B., Slotow, R., Van Aarde, R.J. (2006). A scientific
perspective on the management of elephants in the Kruger National Park and elsewhere
South African Journal of Science. 102, pp. 389-394.
Peel, M. (2012). Ecological monitoring: Association of Private Nature Reserves (Timbavati,
Umbabat, Klaserie and Balule). Tenth Joint Report: 2012, pp. 70. Animal Production
Institute: Livestock Business Division, Nelspruit, South Africa.
Pienaar, U. de V., Van Wyk, P., Fairall, N. (1966). An aerial census of elephant and buffalo
in the Kruger National Park, and the implications thereof on intended management
schemes Koedoe. 9, pp.40-107.
Pringle, R.M. (2008). Elephants as agents of habitat creation for small vertebrates at the patch
scale Ecology. 89 (1), pp. 26-33.
Scholes, R.J. (1997). Savannas In: Cowling, R.M., Richardson, D.M., Pierce, S.M. (eds.)
Vegetation of Southern Africa. Cambridge University Press, Cambridge, pp. 258-277.
Scholes, R.J., Mennell, K.G. (Eds.) (2008). Elephant Management – A scientific assessment
for South Africa.Wits University Press. Johannesburg, South Africa.
Shannon, G., Druce, D.J., Page, B.R., Eckhardt, H.C., Grant, R., Slotow, R. (2008). The
utilization of large savanna trees by elephant in southern Kruger National Park Journal
of Tropical Ecology. 24 (3), pp. 281-289.
Shannon, G., Page, B.R., Duffy, K.J., Slotow, R. (2006). The role of foraging behaviour in
the sexual segregation of the African elephant Oecologica. 150, pp.344-354.
Shannon, G. Mackey, R.L., Slotow, R. (2013). Diet selection and seasonal dietary switch of a
large sexually dimorphic herbivore Oecologia. 46, pp. 48-55.
Sinclair, A.R.E. (1975). The resource limitation of trophic levels in tropical grassland
ecosystems Journal of Animal Ecology. 44, pp. 497-520.
14
Chapter 1
Skarpe, C., Aarestad, P.A., Andreassen, H.P., Dhillon, S.S., Dimakatso, T., Du Toit, J.T.,
Duncan, J.T., Halley, J., Hytteborn, H., Makhsbu, S., Marl, M., Marokane, W.,
Masunga, G., Modise, D., Moe, S.R., Mojaphoko, R., Mosugelo, G., Sejoe, T.,B.,
Stokke, S., Swenson, J.E., Taolo, C., Vandewalle, M., Wegge, P. (2004). The return of
the giants: ecological effects of an increasing elephant population Ambio. 6, pp. 276282.
Smit, I.P.J., Grant, C.C., Whyte, I.J. (2007a). Elephants and water provision: what are the
management links? Diversity and Distributions. 13, pp. 666-669.
Smit, I.P.J., Grant, C.C., Whyte, I.J. (2007b). Landscape-scale sexual segregation in dry
season distribution and resource utilization of elephants in Kruger National Park, South
Africa Diversity and Distributions. 13, pp. 225-236.
Spencer, C. (2010). Motivation for Olifants West Nature – Reserve status.Internal report.
Olifants West Nature Reserve In: Van der Merwe, F., Spencer, C., (2012) Quantifying
the impact of the African Elephant (Loxodonta Africana) on woody plant species within
Olifants West Nature Reserve. Cape Town University of Technology, Republic of
South Africa.
Spencer, C. (2011). From the Wardens Desk – Olifants West December 2011. Internal report.
Olifants West.
Stalmans, M., Attwell, B., Estes, L. (2003). Hunting in the Associated Private Nature
Reserves, Environmental Impact Assessment Process. Final Scooping Report to the
Department of Finance and Economic Development, Limpopo Provincial Government.
Stokke, S, (1999). Sex differences in feeding-patch choice in a megaherbivore: elephants in
Chobe National Park, Botswana Canadian Journal of Zoology. 77, pp. 1723-1732.
Stokke, S., Du Toit, J. (2000). Sex and size related differences in the dry season feeding
patterns of elephants in Chobe National Park, Botswana Ecography. 23, pp. 70-80.
Stokke, S., Du Toit, J. (2002). Sexual segregation in habitat use by elephants in Chobe
National Park, Botswana African Journal of Ecology. 40, pp.360-371.
Thomas, B., Holland, J.D., Minot, E.O. (2011). Seasonal home ranges of elephants
(Loxodonta africana) and their movements between Sabi Sand Reserve and Kruger
National Park African Journal of Ecology. 50, pp. 131-139.
Thomson, P.J. (1975). The role of elephants, fire, and other agents in the decline of a
Brachystegia boehmii woodland Journal of South African Wildlife Management. 5, pp.
11-18.
15
Chapter 1
Thomson, G. (2013). Mohlabetsi South Nature Reserve warden’s Report – 24th June 2013.
Trollope, W.S.W., Trollope, L.A., Biggs, H.C., Pienaar, D., Potgieter, A.L.F. (1998). Longterm changes in the woody vegetation of the Kruger National Park, with special
reference to elephant and fire Koedoe. 41(2), pp. 103-112.
Van Aarde, R.J., Whyte, I., Pimm, S.L., (1999). The consequences of culling for the
dynamics of the African elephant population of Kruger National Park Animal
Conservation. 2, pp. 287-294.
Van Aarde, R.J., Jackson, T.P., Ferreira, S.M. (2006). Conservation science and elephant
management in Southern Africa South African Journal of Science. 102, pp. 385-388.
Van Aarde, R.J., Jackson, T.P. (2007). Megaparks for metapopulations: Addressing the cause
of locally high elephant numbers in southern Africa Biological Conservation. 134, pp.
289-297.
Vanak, A.T., Thaker, M., Slotow, R. (2010). Do fences create an edge-effect on the
movement patterns of a highly mobile mega-herbivore? Biological Conservation. 143,
pp. 2631-2636.
Van de Koppel, J., Prins, H. (1998). The importance of herbivore interactions for the
dynamics of African savanna woodlands: a hypothesis Journal of Tropical Ecology. 4,
pp. 565-576.
Walker, B.H., Stone, L., Vernede, M. (1986). Size structure analysis of the dominant trees in
a South African savanna Journal of South African Botany. 52, pp. 397-402.
Western, D. (1989). The ecological role of elephants in Africa Pachyderm. 12, pp. 42-46.
Western, D. (2003). Conservation Science in Africa and the role of international
collaboration Conservation Biology. 17, pp.11-19.
Whyte, I.J., Biggs, H.C., Gaylard, A., Braak, L.E.O. (1999). A new policy for the
management of the Kruger National Park’s elephant population Koedoe. 42, pp. 111132.
Whyte, I. (2005).Ecosystem resources influencing elephant population: History of the KNP
elephant
populations.
Available
from:
www.sanparks.org/parks/kruger/conservation/scientific/key_issues/4.Population.pdf
[accessed 17th February 2013].
Wittemyer, G., Getz, W.M., Vollrath, F., Douglas-Hamilton, I. (2007). Social Dominance,
seasonal movements, and spatial segregation in African elephants: a contribution to
conservation behaviour Behavioural Ecology and Sociobiology. 61 (12), pp. 19191931.
16
Chapter 1
Wright, J.P., Jones, C.G. (2006). The concept of organisms as ecosystem engineers ten years
on: progress, limitations, and challenges BioScience. 56 (3), pp. 203-209.
Young, K.D., Ferreira, S.M., Van Aarde, R.I. (2009).The influence of increasing population
size and vegetation productivity on elephant distribution in the Kruger National Park
Austral Ecology. 34, pp. 329-342
17
Chapter 2
Chapter 2
Study area and methods
2.1 Study area
The Kruger National Park [hereafter, KNP] was established in 1926 (National Parks Act
No.56 of 1926) but has its roots in the proclamation by Paul Kruger in 1898, when it became
well-known under the name of ‘Sabi Sand Reserve’. The surface area of the park today is
approximately 19.633 km2 (SANParks, 2013). Climatically and geologically the KNP can be
divided into north-south and east-west sections, with the northern part being relatively more
arid than the southern part and the western section being dominated by nutrient-poor granitic
soils, whilst the eastern section provides nutrient-rich basaltic soils (Naiman et al., 2003).
While the western granitic areas are characterised by an undulating terrain with distinct
catenas, these patterns are less substantial in the basaltic east which is rather dominated by
open plains (Venter, 1990). The north-south subdivision is approximately indicated by the
Olifants River. The northern part of the Park receives a mean annual rainfall of between
300mm and 500mm, while the mean annual rainfall south of the Olifants River varies
between 500mm and 700mm. Associated with clayey soils, the mopane shrub
(Colophospermum mopane) represents the predominant woody species of the northern KNP,
while the vegetation community south of the river is dominated by Acacia and Combretum
species (Venter and Gertenback, 1986; Venter et al., 2003). Hot summers and mild winters
characterize the sub-tropical climate of the Lowveld (Venter and Gertenbach, 1986).
Adjacent to the western boundaries of the KNP, the APNR stretches over an area of about
1720km2. The fences between the KNP and the APNR were removed in 1993. The APNR
currently consists of the Balule (363.27 km2), Timbavati (533.92km2), Klaserie (579.18km2)
and Umbabat (240km2) Private Nature Reserves and forms part of the Greater Kruger
National Park. Situated in the semi-arid savanna biome, the APNR is classified as Granite
Lowveld (Mucina and Rutherford, 2006), receiving a mean annual rainfall of less than
600mm (Venter and Gertenbach, 1986). Peel et al. (1993) reported a lower mean annual
rainfall for JPNR. Occupying one-third of South Africa, the savanna biome, defined by its
grass dominated ground layer and upper woody vegetation layer, is the largest biome in
southern Africa, covering 46% of its area (Low and Rebelo, 1996).
1
Chapter 2
Figure 2.1
Map of the study area adjacent to the west of the Kruger National Park (green),
showing the APNR (brown) comprising KPNR, UPNR, TPNR and specifically
Balule PNR, which houses the two study sites OWNR and JPNR.
The study site’s geology is dominated by granitoid rocks originating from the Swazian and
Randian age, predominantly comprising granite, gneiss and migmatite (Venter, 1990). Low
and Rebelo (1996) describe the vegetation of the APNR as Mopani Bushveld, Mixed
Lowveld Bushveld, and Sweet Lowveld Bushveld, whereas Acocks (1975) classified the area
as Acocks 11 veld type and defined the vegetation as Sclerocarya birrea (marula) - Acacia
nigrescens (knob thorn) savanna, distinguishing between Lowveld, Mopani Lowveld and
Arid Lowveld (Acocks, 1988). Further common woody species are Grewia spp. (raisin bush
species), Acacia exuvialis (flaky thorn) and Dichrostachys cineria (sickle bush).
Additionally, several vegetation community patches, which are dominated by a single
species, can be found on the JPNR study site. While Terminalia prunoides (lowveld clusterleaf), for example, dominates the northern tip, an isolated grove of Acacia senegal (three
hook-thorn) is located on the northern ridges of the reserve (Thomson, pers. comm., 2013).
The study was conducted on the Olifants West Nature Reserve (88km2) and on Jejane Private
Nature Reserve (21km2). Both Nature Reserves are located within the Balule Nature Reserve,
with the OWNR joining BNR in 2004 while JPNR removed fences in 2013. OWNR is
located between Hoedspruit and Phalaborwa, thereby representing the western boundary of
2
Chapter 2
the Greater Kruger National Park, while JPNR is situated in the southeastern corner of BNR
adjacent to Klaserie PNR. Before the last fences between BNR and the rest of the APNR
were removed in 2004/5 (Olifants West, 2013; Spencer, 2010), no substantial numbers of
elephants were reported for several decades. After the fence removal however, high numbers
of elephants infiltrated the area (Spencer, 2010). In OWNR, these numbers dropped after the
initial peak. Preliminary results indicated that while breeding herds are presently moving in
and out of the reserve in waves, a number of younger pilot bulls are recorded more constantly
(Spencer, pers. comm., 2013).
Since JPNR joined the APNR by removing fences with BNR and Klaserie PNR in March
2013, similar patterns of elephant influx have been observed in the JPNR. For an area that has
not recorded elephant activity for at least 150 years (Helm, 2011), the impact on the woody
vegetation layer, which was reported to be of great intensity since the fence removal
(Thomson, 2013), has led to noticeable concern among landowners.
2.2
History of elephant distribution in the Greater Kruger National Park
The area that comprises the Kruger National Park today only held a few dozen elephants
between the Letaba and Olifants rivers at the turn of the nineteenth century (Hall-Martin,
1992). However, according to Whyte (2005), estimates prior to 1960 were most likely
unreliable. The population increase observed between 1960 and 1967 (from 1186 to 6586
elephants) probably resulted from a combination of immigration from Zimbabwe and
Mozambique, an optimization of reproductive success and an increase in survival rates
(Whyte, 2005). In 1967, after first concerns about severe impacts on vegetation structure
were raised, a culling regime was implemented with the aim to curb the increase in
population growth and to maintain numbers below 7000 individuals (Pienaar et al., 1966).
The policy of culling lasted for 27 years. Between 1967 and 1994 more than 17 000 elephants
were killed (Van Aarde et al., 2006). In 2002 elephant numbers throughout Southern Africa
were calculated to be around 260 000 (‘The Elephant Status Report 2000’ Blanc et al., 2003).
With the attempt to reduce the spread of the food-and-mouth disease (Joubert, 1996) which
raged in vast areas throughout the country in the 1960s, fences were erected around the KNP,
therefore cutting off the east-west migration of all animals, including elephants. While
estimates indicated a number of approximately 220 elephants within Private Nature Reserves
in the 1970s (Lambrechts, 1974), no elephants existed outside the KNP prior to 1962
(Kettlitz, 1962). After the western boundary fences of the KNP were removed in 1993,
3
Chapter 2
elephants started to move into the Adjacent Private Nature Reserves and numbers within the
APNR increased noticeably (Whyte et al., 1999).
2.3 Methods
Definitions, sampling design and data collection
2.3.1 Data analysis
A detailed description of the statistical analyses is given separately in each chapter. The
following
software
packages
were
used:
MINITAB
16©
for
statistical
tests,
EstimateSWin820© to calculate diversity indices and to create rarefaction curves and
Microsoft EXCEL© for graphical and descriptive statistics.
2.3.2 Tree – shrub definition
Trees and shrubs were defined according to a combination of definitions by Van Wyk and
Van Wyk (2007) and commonly used international descriptions (FAO 1998, FAO 2004,
UNECE/FAO 2000).
“The three main characteristics of trees are:
Trees are woody perennial plants.
Trees have one single main stem but can have several stems in the case of coppice.
Trees have a comparatively definite crown.”
“The three main characteristics of shrubs are:
Shrubs are woody perennial plants.
Shrubs frequently lack a definite crown.
The height of shrubs in general varies between 0.5 m and 5 m.”
Species that were not described as typical and distinct tree or shrub species but adopted by
both categories (Van Wyk and Van Wyk, 2007), depending on the individual physiognomy,
have been recorded as shrubs if they were: multi-stemmed, equal to or smaller than 2m in
height (≤ height class 4) and lacking a definite crown. Such species were:
Acacia erubescens (blue thorn), and Acacia exuvialis (flaky thorn), Capparis tomentosa
(woolly caper-bush), Dichrostachys cinerea (sickle bush), Ehretia amoena (sandpaper bush)
and Terminalia prunoides (lowveld cluster-leaf).
4
Chapter 2
2.3.3 Sampling design and data collection
Field work was conducted during the late dry season, starting at the end of July 2013 and
ending with the first important rain at the beginning of November 2013. A combination of
two different sampling techniques was chosen to provide for a sound methodology and
applied as follows:
1. Vegetation sampling along permanent transects.
2. Plant-based surveys of food plots along fresh (not older than 12 hours) feeding paths
(hereafter referred to as ‘backtracking’), in combination with personal observation of
foraging behaviour.
2.3.3.1 Transect sampling
The OWNR study site houses two parallel survey transects (Appendix A1.1) of 7km and 9km
length. The two transects were already set up but were extended for this study and species
were again identified for reconfirmation. Transects are orientated north-south, running
parallel to the western boundary fence landward from the Olifants River. This design was
chosen because it considers the gradient of change in the vegetation community structure and
composition across the landscape as well as providing information on the gradient of impact
when moving inland from riparian habitats. Vegetation plots are 10m2 in size and located
10m off the road, every one kilometre on each transect, resulting in seven and nine survey
plots respectively. On the JPNR study site two transects (Appendix A1.2), perpendicular to
each other, running north to south and west to east were set up. Transects were of 5km and
7km in length, hence accounting for five and seven vegetation plots. As the MSNR has no
perennial riparian system, the location of the two transects was chosen to represent the
orientation of the former fence line, i.e. the present route of entry that the elephants are using.
Sample sizes differed between study sites because of the unequal reserve sizes. In both cases,
transects ran from river/fence to fence, so that it was impossible to further extend any of
them. After a ‘once-off’ baseline recording, which documented impact that had been
accumulated over time and was referred to as ‘old’ impact, all transects were sampled
biweekly in order to record any ‘new’ impact on trees and shrubs caused by elephants. All
woody plants rooted within a plot were physically marked with a numbered tag and identified
to species level. Grewia and Commiphora spp. were identified to genus level as late dry
season identification to species level became inaccurate.
Impact was only noted down if it could be allocated to elephants, and when other browsers
could be ruled out as the cause of damage. Therefore, elephants were described as the agent
5
Chapter 2
of cause for utilization events like bark-stripping, uprooted trees, large broken-off branches
and main stem breakage. Canopy branches and smaller branches that were freshly browsed
by elephants could be confidently identified with the branch ending usually left in a bristly
shape caused by the way they twist the branch with their trunks when breaking it off (pers.
observ.). For each woody plant individual within the survey plots, the height class was
recorded, whether the species was single-stemmed or multi-stemmed, and whether it was
defined as shrub or tree species. The height of each tree and shrub was measured by the use
of a measuring pole, which had markings every 0.5m starting at 0 and ending at 5m. The
following ten height classes were distinguished: 0 to <0.5m, 0.5m to <1m; 1m to <1.5m,
1.5m to <2.m, 2m to <2.5m, 2.5m to <3m, 3m to <3.5m, 3.5m to <4m, 4m to <4.5m and
>4.5m. On each single visit, every tagged plant was sampled by noting down the potential
impact type (uprooting, branch breaking, main stem breakage, bark-stripping, breaking-off of
larger branches to access smaller plant parts), impact intensity and the plants phenological
state. The extent of impact for each of the distinct impact mode categories was recorded
according to eight classes (1-5%, >5-10%, >10-25%, >25-50%, >50-75%, >75-90%, >9099%, 100%), which described the proportional loss of biomass of a crown, trunk or root
system. The mode of data recording was adopted from Greyling (2004) and Zambatis (2005)
and modified in consultation with my supervisors. For consistency purposes, each and every
recording was made by the same person.
In the context of the growing concern with regard to the decline in large trees, in particular of
marula trees (Sclerocarya birrea), the vegetation plots were extended to account for a
reasonable sample size, as marula trees were rarely represented within the original survey
plots themselves. The existing plots were extended by straight lines of 50m length,
perpendicular to each other and radiating from the centre of the original plot into the three
directions that did not hit the road. The resulting extension plots were thus 5000m2 in size.
Within these extensions, all trees of the species Marula (Sclerocarya birrea), false marula
(Lannea schweinfurthii) and knob thorn (Acacia nigrescens), species that were identified to
be of particular concern to landowners and managers, were physically tagged and the basal
circumference measured. After the baseline data had been recorded, the extension plots were
revisited three times during the study period and the tagged trees sampled in the same fashion
as the original survey plots.
6
Chapter 2
2.3.3.2 Plant-based surveys of food plots along fresh feeding paths
Plant-based surveys were adopted from the methods used by Greyling (2004), Stokke (1999)
and Zambatis (2005). Two techniques were applied in order to find fresh (not older than 12
hours) elephant foraging sites. First, a set route (referred to as the ‘elephant route’) was
planned in consultation with the wardens on both study sites (Spencer, C. [OWNR] and
Thomson, G. [JPNR]) and each driven two days a week. Secondly, all available information
on the day’s or previous day’s elephant sightings were gathered from landowners and field
rangers.
The ‘elephant routes’ were 48km in length on OWNR (Appendix A 1.1) and 43km in length
on JPNR (Appendix A 1.2). Once elephants were encountered, the car was stopped and field
observations started. The data that was recorded during these surveys included time, date,
GPS co-ordinates (location), herd demographics (sex, age estimation, group composition) and
activity (feeding, travelling, resting, drinking, wallowing, swimming). In the case of feeding
and where possible, the plant species, feeding mode, plant part and impact class (low,
medium, high) were recorded. The elephants that were most easily visible were chosen as
focus individuals. If the animals remained at the location, with no intention of moving off, for
longer than 30 minutes, the location was abandoned and returned to at a later point within 12
hours, where possible. Otherwise, plant-based surveys (‘backtracking’ of the feeding path)
were conducted once the animals had moved off. Circular plots were described by a 5m
radius around a woody plant species that had been freshly browsed on, whereby this ‘centre’
species was always the closest newly impacted species encountered along the feeding path, in
order to avoid subjective decisions. According to Greyling (2004), a plant was considered to
have been freshly utilized if the sap of impacted stems or removed and discarded plant parts
had not yet dried out or changed in colour. Along the feeding path, two to eight consecutive
food plots were defined. The individual plots were at least 20m apart in order to prevent
autocorrelation between sampling units. This distance, which was measured by pacing with
one pace averaging 1m, was chosen because Greyling (2004) proved it to be necessary to
allow for interdependence of plant species utilization and availability. The technique of the
data recording for each food plot was identical to the method used for the transect survey,
whereby all utilized and non-utilized woody plant individuals were monitored.
Unless otherwise indicated, it was common practice throughout this study to distinguish
between ‘new’ impact data and ‘old’ impact data. ‘Old’ impact data accounted for the first
baseline sampling of the transect vegetation plots and for the recording of impact that could
not be regarded as ‘new’ when sampling fresh feeding paths (backtracking method). ‘New’
7
Chapter 2
impact data described all ‘fresh’ recordings from the backtracking survey technique and data
that was sampled on the transect vegetation plots every fortnight, hence accounting for
impact that was not older than two weeks.
8
Chapter 2
2.4 References
Acocks, J.P.H. (1975). Veld types of South Africa Memoirs of the Botanical Survey of South
Africa. 40, pp. 1-128.
Acocks, J.P.H. (1988). Veld types of South Africa. 3rd. ed. Memoirs of the Botanical Survey
of South Africa 57. Department of Agricultural Technical Services, Pretoria, ZA.
Blanc, J.J., Thouless, C.R., Hart, J.A., Dublin, H.T., Douglas-Hamilton, I., Craig, C.G.,
Barnes, R.F.W. (2003).African Elephant Status Report2002. International Union for
Conservation of Nature and Resources, Gland, Switzerland and Cambridge, UK.
FAO (1998). The Global Forest Resources Assessment 2000 (FRA 2000): Terms and
definitions. FRA Working Paper 1, FAO Forestry Department Rome.
FAO (2004). Global Forest Resources Assessment update 2005 (FRA 2005): Terms and
definitions. FRA Working Paper 83/E, FAO Forestry Department Rome, 34 pages.
Greyling, M.D. (2004). Sex and age related distinctions in the feeding ecology of the African
elephant, Loxodonta africana. PhD Thesis, Faculty of Science, University of the
Witwatersrand, Johannesburg.
Hall-Martin (1992). Distribution and status of the African elephant Loxodonta Africana in
South Africa, 1652-1992 Koedoe. 35, pp. 65-68.
Helm, C.V. (2011). Investigating the life history strategy of an African savannah tree,
Sclerocarya birrea subsp. caffra (marula) PhD Thesis, Faculty of Science, University
of the Wiwatersrand, Johannesburg, South Africa.
Jejane (2013). History – timeline for Jejane Private Nature Reserve. Available from:
www.jejane.co.zza/tags/history [accessed 13th Februaury 2013].
Joubert, S.C.J. (1996). Master plan for the management of the Associated Private Nature
Reserves.
Kettlitz, W.K. (1962). The distribution of some of the large game mammals in the Transval
(excluding the Kruger National Park) Annals of the Cape Provincial Museum. 11, pp.
118-137.
Lambrechts, A. von W. (1974). The numerical status of sixteen game species in the
Transvaal, excluding the Kruger National Park Journal of the Southern African Wildlife
Management Association. 64, pp. 1-37.
Low, A.B., Rebelo, A.G. (eds.) (1996). Vegetation of South Africa, Lesotho and Swaziland.
Dept. Environmental Affairs & Tourism, Pretoria.
9
Chapter 2
Mucina, L., Rutherford, M.C. (2006). The vegetation of South Africa, Lesotho and Swaziland
Strelitzia. 19, pp. 348-437.
Naiman, R.J., Braak, L.E.O., Grant, C.C., Kemp, A.C., Du Toit, J.T., Venter, F.J. (2003).
Interactions between species and ecosystem characteristics. IN: Du Toit, J.T., Rogers,
K.H., Biggs, H.C., (eds.) The Kruger Experience, Ecology and management of savanna
heterogeneity. Long Island press, Washington, pp. 221-241.
Olifants West (2013). OWNR – Introduction and Background. Available from:
www.olifantswest.co.za [accessed 13th February 2013]
Peel, M.J.S., Peel, J.M.H., Montagu, G.P. (1993). An ecological review and survey of Vienna
Game Ranch. Unpublished ARC-RFI report to landowner, Nelspruit.
Pienaar, U. de V., Van Wyk, P., Fairall, N. (1966). An aerial census of elephant and buffalo
in the Kruger National Park, and the implications thereof on intended management
schemes Koedoe. 9, pp. 40-107.
SANParks
(2013).
Kruger
National
Park
–
Did
you
know?
Available
from:
www.sanparks.org/parks/kruger/tourism/history.php [accessed 17th February 2013].
Carruthers, J., Boshoff, A., Slotow, R., Biggs, H.C., Avery, G., Matthews, W. (2008). The
elephant in South Africa: history and distribution In: Scholes, R.J., Mennell, K.G. (eds.)
Elephant Management - A Scientific Assessment for South Africa. Wits University
Press, Johannesburg, South Africa, pp. 23-83.
Spencer, C. (2010). Motivation for Olifants West Nature – Reserve status.Internal report.
Olifants West Nature Reserve In: Van der Merwe, F., Spencer, C., (2012) Quantifying
the impact of the African Elephant (Loxodonta Africana) on woody plant species within
Olifants West Nature Reserve. Cape Town University of Technology, Republic of
South Africa.
Stokke, S, (1999). Sex differences in feeding-patch choice in a megaherbivore: elephants in
Chobe National Park, Botswana Canadian Journal of Zoology. 77, pp. 1723-1732.
Thomson, G. (2013). Mohlabetsi South Nature Reserve warden’s Report – 24th June 2013.
UECE/FAO (2000). Forest resources of Europe, CIS, North America, Australia, Japan and
New Zealand Geneva and Forest study papers 17. United Nations, New York and
Geneva.
Van Aarde, R.J., Jackson, T.P., Ferreira, S.M. (2006). Conservation science and elephant
management in Southern Africa South African Journal of Science. 102, pp. 385-388.
Van Wyk, B., Van Wyk, P. (2007). Field guide to trees of Southern Africa. Joyce, P. (ed.).
Struik Publishers, Cape Town, South Africa, pp. 536.
10
Chapter 2
Venter, F.J., Gertenbach, W.P.D. (1986). A cursory review of the climate and vegetation of
the Kruger National Park Koedoe. 29, pp. 139-148.
Venter, F.J. (1990). A classification of land for management planning in the Kruger National
Park. PhD Thesis, University of South Africa, Pretoria.
Venter, F.J., Scholes, R.J., Eckhardt, H.C. (2003). The abiotic template and its associated
vegetation pattern IN: Du Toit, J.T., Rogers, K.H., Biggs, H.C. (eds.) The Kruger
Experience, Ecology and Management of Savanna Heterogeneity. Long Island Press,
Washington, pp. 83-129.
Whyte, I.J., Biggs, H.C., Gaylard, A., Braak, L.E.O. (1999). A new policy for the
management of the Kruger National Park’s elephant population Koedoe. 42, pp. 111132.
Whyte, I. (2005).Ecosystem resources influencing elephant population: History of the KNP
elephant
populations.
Available
from:
www.sanparks.org/parks/kruger/conservation/scientific/key_issues/4.Population.pdf
[accessed 17th February 2013].
Zambatis, N. (2005). Field procedures for veld condition assessment in the Kruger National
Park.
Scientific
Services,
Kruger
11
National
Park,
2nd
Revision.
Chapter 3
Chapter 3
Vegetation structure and composition of two Nature Reserves located
within the semi-arid savanna biome
3.1 Introduction
Recording baseline data of the site-specific vegetation structure and composition is equally
significant for reasons of comparison between sites, as well as for future reference purposes
in long-term monitoring programmes within sites. Such data records allow for the detection
of a shift in abundance of structural (i.e. height classes, tree and shrub cover, number of
stems) and compositional (species richness and diversity) variables from the benchmark.
Ecosystems of the semi-arid savanna biome are characterized by the fluctuating and dynamic
co-occurrence of a continuous grass layer associated with an intermittent layer of woody
stands (Langevelde et al., 2003; Scholes and Archer, 1997). Multiple studies have been
conducted in order to determine the variables which are driving shifts in the tree-grass ratio in
general and changes in the woody vegetation structure in particular. Results are divergent in
detail but consistent with the general opinion that the vegetation structure of savanna
ecosystems are shaped by an interaction among biotic and abiotic aspects. Potential factors
that have been agreed to be of significance are topography, rainfall, drought, geomorphology,
fire and herbivory (e.g. Backeus, 1992; Bond et al., 2001; Dublin et al., 1990; Langevelde et
al., 2003; Pellew, 1983; Rogers and O’Keeffe, 2003; Sankaran et al., 2005, 2008; Scholes
and Archer, 1997).
Amongst herbivores, which exploit 30% to 60% of the primary production and hence
significantly contribute to the dynamics of savanna ecosystems (McNaughton et al., 1989;
Senft et al., 2005), elephants are widely considered as ecological engineers (Jones et al.,
1994) and often described as the principal agent of habitat change (Laws, 1970; Pringle,
2008; Thomson, 1975). The browsing consequences have been recorded to affect the
distribution and composition of the vegetation as well as to influence its structure (Augustine
and McNaughton, 2004; Bond and Loffell, 2003). Because the two study sites (OWNR and
JPNR) have experienced a different history with regard to the recent presence of elephants, it
is of interest to compare the vegetation structure and composition of these two Nature
Reserves.
Kemp et al. (1997) explained the driving force for distribution, density and range of any
species by its preference and choice of habitat. Hence, the alteration in structural or
1
Chapter 3
compositional habitat elements may have severe consequences for a wide range of species
and additional aspects of the biological diversity. Accordingly, vegetation structure itself can
be used as an indicator or a measure of surrogate for certain elements of the site specific
biodiversity. Therefore, understanding shifts in structure and composition of the woody
vegetation layer becomes essential for potential management decisions. For example, a
decrease in the abundance of tall trees with a simultaneous increase in small to intermediate
species might indicate bush encroachment, as might a shift from tree to shrub species,
implying potentially severe consequences for the indigenous biodiversity. On the other hand,
a simultaneous decrease in reproductively mature trees and in seedlings could predict a future
lack of recruitment, again resulting in the potential limitation of resources for many species.
Woody species not only enhance the site specific structural diversity but equally contribute to
the site specific compositional diversity. A shift in the relative abundance of certain species
or the extinction of indigenous species may have serious consequences for the site specific
biodiversity as a whole (Pickett et al., 2003). This again implies that the early detection of a
change in vegetation composition can be indicative for system change, therefore allowing for
‘on time’ management reactions.
Furthermore, woody species, notably large trees, benefit ecosystem processes such as
hydraulic lift (Ludwig et al., 2008), thereby supplying water for neighbouring plants and
promoting nutrient cycling and plant growth (Horton and Hart, 1998), as well as erosion
control (Rogers and O’Keeffe, 2003). Parallel to these ecological contributions, the aesthetic
value of large trees in general and of rare or vulnerable species in particular, are of great
significance to managers and the general public (Owen-Smith et al., 2006). The concern of
losing large trees has mounted in accordance with the increasing evidence of a decline in the
large tree population over the past decades (Grant and Kruger, 2007). Marula (Sclerocarya
birrea), a dominant and keystone species within the woody plant community (Shakleton et
al., 2002; Van Wyk, 1974) is valued for its ecological, economic and cultural importance
(Wynberg et al., 2002), being widely utilized by humans and wildlife (Shackleton et al.,
2002). Numerous studies have reported unstable marula populations as well as locally extinct
populations and discussed causes such as elephant impact, herbivory, fire and episodic
recruitment events (Helm et al., 2009; Jacobs et al., 2002a,b; Lewis, 1987; Walker et al.,
1986). Although found to be extremely resistant to herbivory and fire once established, Helm
et al. (2011) concluded that excessive utilization by elephants and recurrent fire could be
fatal. Protective management authorities, which are involved in the debate of the ‘elephant
problem’, are often particularly concerned about the population structure of Sclerocarya
2
Chapter 3
birrea and Acacia nigrescens, two species highly favoured and selected by elephants (OwenSmith, 1988; Shannon et al., 2008; Trollope et al., 1998). The results of a questionnaire
survey distributed within the APNR in 2003 revealed that the decline of marula (Sclerocarya
birrea), knob thorn (Acacia nigrescens) and false marula (Lannea schweinfurthii) were of
special concern to residents and tourists (Henley, 2013) and hence promoted the requirement
of species-specialized monitoring programmes.
A number of studies have found that high elephant population densities and the species’
selective feeding behaviour can drive a substantial change in woody vegetation structure and
composition (Barnes, 1983; Wing and Buss, 1970), consequently leading to a decrease in
habitat refugia for habitat-sensitive and habitat-dependent species (Cumming et al., 1997).
However, Grant and Kruger (2007) stated that elephant densities in the Kruger National Park
were possibly still too low to have a substantial effect on species diversity and indicated that
sound study results were furthermore lacking while the complexity of interacting aspects that
drive the change of savanna ecosystems were still poorly understood. Moreover, an additional
theory is that cycles of habitat disturbance by elephants, such as woodland clearance and
species loss, are ‘natural’, have always been part of savanna ecosystems and are furthermore
essential in order to ensure long-term habitat stability, resilience and biodiversity in general
(Caughley, 1980; Cumming et al., 1997). Considering the widely divergent results and
opinions, additional studies are necessary in order to justify the concern that elephant feeding
behaviour leads to a severe change or even loss of structural and compositional diversity.
3.1.1 Objectives
The objectives of Chapter 3 were to obtain baseline records on vegetation structure and
community of the two study sites in order to allow for a better understanding of the current
system condition. In consideration of the above literature review, both analysis and
discussion were based on the following assumptions and predictions:
The utilization of the woody vegetation layer by elephants for a period approximately
eightfold longer on OWNR was predicted to depress population stability by impacting
reproductively mature plants and impairing recruitment, consequently causing an alteration in
vegetation structure and composition to a more profound extent than would be observed on
JPNR. Differences in the structural and compositional diversity between the two study sites
were therefore expected to be noticeable.
The population structure of Sclerocarya birrea and Acacia nigrescens were expected to be
similar as both species had had long term exposure to the same habitat and climatic
3
Chapter 3
conditions and are furthermore highly selected by browsers in general. The potential
alteration of the population structure should therefore not be species specific.
3.2 Methods
Refer to Chapter 2 for a detailed description of the study area and the survey methods.
3.3 Data analysis
Data records of study sites were treated separately but results of the two survey methods
(backtracking and transect sampling) were generally pooled with the objective of increasing
site specific sample size and therefore maximizing confidence when testing for differences in
vegetation structure and composition between study sites.
3.3.1 Vegetation structure
The number of trees and shrubs was counted separately for each study site and a contingency
table was constructed. The chi-square (χ2) test was used to evaluate whether the differences
between the observed frequencies and the expected frequencies were statistically significant,
thereby testing a potential association between the abundance of shrubs and trees and the
location. To allow for a simple comparison of the relative abundance of trees and shrubs
between study sites, the site specific proportions of available trees and shrubs were calculated
and plotted.
Furthermore, the number of single-stemmed and multi-stemmed trees and shrubs was counted
for each study site in order to allow for evaluation of an additional structural variable. To test
whether there was a statistically significant difference between the site specific expected and
observed frequency of single-stemmed and multi-stemmed trees and shrubs respectively,
contingency tables were created. Subsequently a chi-square (χ2) test was conducted to
determine the potential interaction between the number of stems (single-stemmed or multistemmed) and the location of trees and shrubs respectively.
The total frequency of woody plant individuals within each of the ten height classes was
counted for each study site. To test for a potential association between the variables height
class availability and study site, a chi-square (χ2) test was carried out, thereby determining a
potential difference in the frequency distribution of height classes between study sites.
4
Chapter 3
3.3.2 Vegetation composition
To test for site specific characteristics with regard to the vegetation composition, indices for
species richness, evenness and diversity were calculated.
A plain and possibly the most intuitive way is to describe vegetation communities by use of a
species richness measure (S) (Magurran, 2004; McIntosh, 1967), i.e. counting the number of
different species per sampling or study unit. Although a measure of species richness will
assist in evaluating and comparing vegetation communities and habitats (Humphries et al.,
1995), diversity indices such as Simpson’s index (D) (Simpson, 1949) are crucial when
assessing biological diversity, because species richness alone does not provide an
understanding of the driving ecological mechanisms (Begon et al., 2006).
Simpson’s index was chosen to be most suitable as a heterogeneity index (Good, 1953;
Hurlbert, 1971) because, in contrast to the Shannon-Weaver index (H) (Shannon and Weaver,
1949), it is weighted towards the most abundant species and only marginally sensitive to
species richness. Furthermore, Simpson’s index was described as being of prime robustness,
combining species richness with evenness in its calculation and taking into account the
variance of the species abundance distribution (Magurran, 2004).
However, instead of Simpson’s index of similarity (D):
D = ∑ pi2 where pi represents the proportion of individuals in the ith species, D giving the
probability that any two randomly drawn individuals from an infinite sample belong to the
same species and values range from 0 = high diversity to 1 = low diversity,
Simpson’s diversity index of dissimilarity (1-D) was calculated, allowing for a better
interpretation as values range from 0 = low diversity to 1 = high diversity:
1-D = (1-D) = {1-[1/(1/D)]} giving the probability that any two randomly drawn individuals
from an infinite sample belong to a different species (Magurran, 2004).
In order to describe how species were distributed in space, i.e. the variability in species
abundance, Simpsons’s measure of evenness (E) was calculated. This measure of evenness is
not sensitive to species richness and values range from 0 = low evenness to 1 = high evenness
(Magurran, 2004):
E = ((1/D)/S) where E = evenness, (1/D) = reciprocal Simpson’s index of diversity and S =
observed number of species.
To test whether indices (Simpson’s diversity index, Evenness and Species richness) differed
between the two study sites, a Mann-Whitney U-test was carried out. Because this test is
distribution-free, it was described as suitable for testing diversity indices (Fowler et al.,
5
Chapter 3
1998). In order to provide a sufficient number of observations for each sample, the separately
calculated diversity indices for transect and backtracking methods of each reserve were used.
To determine the relation between sampling effort and sampling success (Hortal et al., 2006),
thereby judging sampling efficiency, the cumulative number of species as a function of
sampling effort (Colwell and Coddington, 1994) was plotted for each reserve, thereby
generating sampling efficiency curves. Each vegetation plot (transects) and each feeding path
(backtracking) represented an independent sample. The resulting rarefaction curves can,
according to Gotelli and Colwell (2001), be regarded as the ‘statistical expectation of the
corresponding accumulation curve’ and are based on the observed species richness (Sobs
Mao Tao). The software EstimateS© that computes Monte Carlo re-sampling, in which
samples are randomly accumulated during many iterations (Colwell, 2009) was used. A list
of all species that were sampled during the study period is given in Appendix B.1.
For the estimation of marula ages, a regression equation (age regressed against basal
circumference) was adapted from A.W. Haig (1999) and used for inter- and extrapolation,
therefore allowing for an interpretation of the age structure of Sclerocarya birrea that was
recorded within the two study sites (Appendix B.2). Haig’s (1999) results were based on the
data of marula trees that were sectioned, age estimated and of which the basal circumferences
were measured. After testing for normality, a two-sample t-test was carried out to determine
whether the age distribution of Sclerocarya birrea differed between study sites. In order to
illustrate the distribution of age, nine age classes of 20 year intervals were created and study
sites plotted against each other. Trees of the species Acacia nigrescens were classified in nine
classes that corresponded to the categories of basal circumferences which resulted from the
classification of Sclerocarya birrea age classes. Because sample sizes were too small to apply
any statistical test, descriptive statistics were used in order to evaluate potential differences
between the site-specific distribution of the population demographics of Sclerocarya birrea
and Acacia nigrecens respectively, as well as between Sclerocarya birrea and Acacia
nigrescens populations when pooled across study sites. Lannea schweinfurthii (false marula)
was not included in any analysis as sample sizes were extremely small.
6
Chapter 3
3.4 Results
3.4.1 Vegetation structure
No statistically significant interaction between the variables ‘location’ and the ‘number of
available trees or shrubs’ was found (chi-square test, χ2 = 3.543, df = 1, P = 0.06). This result
was well illustrated when comparing the relative proportion of trees and shrubs between
study sites, with the vegetation on OWNR consisting of 55% shrubs and 45% trees while the
vegetation on JPNR consisted of 60% shrubs and 40% trees (Figure 3.1), demonstrating only
a small discrepancy of 5% for each category between study sites. The vegetation within both
study sites was dominated by shrubs.
Relative proportion of abundance
100%
90%
80%
70%
55%
60%
60%
50%
shrubs
40%
trees
30%
20%
45%
40%
OWNR
JPNR
10%
0%
Study sites
Figure 3.1
Relative proportions of available shrubs and trees - study sites compared.
Study sites did not differ significantly with regard to the abundance of single-stemmed versus
multi-stemmed shrubs (chi-square test, χ2 = 0.754, df = 1, P = 0.385), as reflected when
plotting the relative proportion of available shrubs and trees in each category (Figure 3.2),
with the percentage of single-stemmed shrubs being 5% on OWNR and 7% on JPNR and the
percentage of multi-stemmed shrubs being 95% on OWNR and 93% on JPNR respectively.
Multi-stemmed shrubs as opposed to single-stemmed shrubs clearly dominated on both study
sites.
7
Chapter 3
Relative proportion of abundance
100%
90%
5%
7%
80%
70%
single-stemmed
shrubs
60%
50%
40%
95%
93%
30%
multi-stemmed
shrubs
20%
10%
0%
OWNR shrubs
JPNR shrubs
Study sites
Figure 3.2
Relative proportions of single-stemmed and multi-stemmed shrubs within each
study site - study sites compared.
However, when testing for an association between the frequency of single-stemmed and
multi-stemmed trees and the location (Figure 3.3), the result (chi-square test, χ2 =5.691, df =
1, P = 0.017) proved to be significant, implying an interaction between location and number
of stems. This was mirrored by the relative proportion of trees in each category and signifies
that the relative proportion of single-stemmed trees (55%) on JPNR was lower than on
OWNR (65%), while the relative proportion of multi-stemmed trees (45%) on JPNR was
higher than on OWNR (35%).
Relative proportion of abundance
100%
90%
80%
70%
55%
65%
single-stemmed
trees
60%
50%
40%
multi-stemmed
trees
30%
20%
45%
35%
10%
0%
OWNR trees
JPNR trees
Study sites
Figure 3.3
Relative proportions of single-stemmed and multi-stemmed trees within each study
site - study sites compared.
8
Chapter 3
No association (chi-square test, χ2 =15.453, df = 9, P = 0.079) was found between the
variables ‘distribution of height classes’ and ‘location’ (Figure 3.4), implying that the
distribution of height classes was not site specific. This result was important as it justified the
pooling of corresponding data across reserves in later chapters.
Frequency of occurrence
180
160
140
JPNR
120
100
OWNR
80
60
40
20
0
Height classes 1-10, in metres
Figure 3.4
Distribution of height classes across each study site - study sites compared.
Figure 3.4 emphasizes the statistical insignificance of the test result by the representation of
an almost identical overall shape, indicating a similarity of height class distribution between
reserves. On both study sites the representation of the smallest height class (0 - 0.5m) was
low (1% on both reserves), followed by a gradual increase which found its peak (24% on
OWNR, 22% on JPNR) within height class four (1.5m - 2.0m). Following this peak, the
frequency of occurrence within each height class gradually decreased until height class nine
(4.0m - 4.5m), being represented with 4% on both reserves and succeeded by an abrupt rise in
the availability of woody species (12% on OWNR, 13% on JPNR) within height class ten
(>4.5m). Most prolific were height classes between 1m and 2.5m and above 4.5m.
9
Chapter 3
3.4.2 Vegetation composition
Species richness (Mann-Whitney test, W = 20.5, n = 4, P = 0.559 adjusted for ties), Evenness
(Mann-Whitney test, W = 18.5, n = 4, P = 1.0 adjusted for ties) and Species diversity (MannWhitney test, W = 19.5, n = 4, P = 0.771 adjusted for ties) did not differ significantly
between study sites, implying no marked site specific characteristics with regard to the
vegetation composition.
These results were supported by the fact that biodiversity indices of the pooled data (pooling
of vegetation recordings from transects and feeding paths), which allowed for an evaluation
of a larger spatial scale, were found to be identical between study sites: Species diversity
(1-D = 0.7) was relatively high, while Evenness was low (E = 0.2), implying an uneven
distribution of species at a spatial scale. The same amount of different species (S = 23) was
observed on both study sites.
Table 3.1
Biodiversity indices - study sites compared.
Sampling unit
Transect 1
Transect 2
Backtracking
Pooled data (transect+backtr.)
Simpson's diversity index (1- D )
OWNR
JPNR
0.8
0.8
0.5
0.5
0.8
0.7
0.7
0.7
Eveness ( E )
OWNR
JPNR
0.4
0.3
0.2
0.2
0.2
0.2
0.2
0.2
Species richness ( S )
OWNR
JPNR
13
12
13
10
22
21
23
23
Regardless of location, the sample-based rarefaction curve (Figure 3.5) approached the
plateau but did not reach an asymptote, implying that the sampling effort was not sufficient to
reflect true species richness of the local species assemblage. High R-squared (r2) values, of
0.98 for both study sites, indicated that the variation of the response variable (Species
richness) was well explained by the model (98%), i.e. data and model had a good fit.
10
Chapter 3
Cumulative no. of observed species
(a)
25
20
15
y = 5.9578ln(x) + 2.6703
R² = 0.9811
10
5
0
0
5
10
15
20
25
30
No. of samples
Cumulative no. of observed species
(b)
25
20
15
y = 5.4627ln(x) + 3.158
R² = 0.9839
10
5
0
0
5
10
15
20
25
30
35
No. of samples
Figure 3.5
Rarefaction curve for OWNR (a) and JPNR (b), based on the overall (pooled)
cumulative no. of species.
The low value (0.2) of Simpson’s measure of evenness was well illustrated by the two graphs
as the rise of the curves was relatively slow, indicating an uneven distribution of few
common and many less common species.
11
Chapter 3
With the estimated mean age of Sclerocarya birrea being 85.1 years on OWNR as opposed to
103.3 years on JPNR, a significant difference (two-sample t-test, t = 3.03, df = 85, P = 0.003)
was tested between study sites, implying an older population on JPNR.
Frequency of occurence
14
12
10
8
6
OWNR
4
JPNR
2
0
Age classes 1-9, in years
Figure 3.6
Frequency distribution of Sclerocarya birrea across age classes - study
sites compared.
Figure 3.6 illustrates the relative abundance of trees within different age classes, thereby well
reflecting the test result, with the distribution of the JPNR population being negatively
skewed while the one on OWNR could be described as rather bell-shaped. Both populations
were under-represented within the very young age classes (0 - 19yrs, 20yrs - 39yrs) (OWNR
= 8%, JPNR = 2%) and within the very old age classes (140yrs - 159yrs, 160yrs -179yrs)
(OWNR = 4%, JPNR = 10%). The frequency distribution of individuals on OWNR increased
steadily from the youngest age class (0-19yrs), peaking (25%) with age class five (80yrs 99yrs), followed by a gradual decrease up to age class eight (140yrs - 159yrs), represented by
4% of sampled trees and missing in age class nine (160yrs -179yrs). Age classes four to six
(60yrs - 119yrs) were most prolific, represented by 67% of the sampled individuals.
In contrast, no trees in the youngest age class were sampled on JPNR, and age classes two
(20yrs - 39yrs) and three (40yrs - 59yrs) only had one representative each. Trees were most
abundant (85%) in age classes four to seven (60yrs - 139yrs), with the greatest representation
(27%) in age class six (100yrs - 119yrs), but were rarely sampled (10%) in age classes eight
and nine (140yrs - 179yrs). Both populations could therefore be described as adult
dominated, with age classes four to six (60yrs - 119yrs) being most prolific, regardless of
location.
12
Chapter 3
When analysing the demographics of the Acacia nigrescens population (Figure 3.7) for both
study sites, the distribution of basal circumference classes could be described as positively
skewed. While no trees were sampled in the smallest class (0-25cm), only a few were
recorded in class two (26cm - 51cm). On OWNR, 67% of the sampled trees fell within
classes three and four (52cm - 106cm), 26% of the trees were almost equally spread between
class five to eight (107cm - 210cm) and only one individual was sampled within class nine
(211cm - 235cm). On JPNR, 8% of the sampled individuals fell within class two (26% 51%), 32% within class three (52cm - 78cm) and 32% were represented by classes six and
seven (130cm -180cm). Class four included 14% and classes five (107cm - 129cm), eight and
nine (181cm -235cm) shared the remaining 14 % of the sampled trees.
To summarize, it can be said that the representation of the distinct classes was heavily
weighted towards classes three and four (52cm - 106cm) on OWNR, while class three
(52cm - 78cm) was the most prolific on JPNR. Assuming that basal circumference is
positively correlated with age, this implies that populations on both study sites could be
described as youthful to mature rather than as senescent, and this statement was in particular
Frequency of occurence
true for OWNR.
18
16
14
12
10
8
6
4
2
0
OWNR
JPNR
Classes of basal circumference measurements, in cm
Figure 3.7
Frequency distribution of Acacia nigrescens across ‘basal circumference classes’
- study sites compared.
Results of the population structure for Sclerocarya birrea and Acacia nigrescens were pooled
across study sites (Figure 3.8) in order to inflate sample sizes and to get a general idea of the
species specific demographics within the area, regardless of site specific characteristics. It
must be noted that, despite pooling of results across reserves, the shape of the graphs
remained consistent for both species with a more bell-shaped distribution for Sclerocarya
birrea and a positively skewed distribution, heavily weighted towards lower to medium
classes, for Acacia nigrescens. These distribution patterns indicated that the marula
13
Chapter 3
population, which was represented by very few young and very few senescent individuals
with the majority (80%) made up of mature trees, was older and supported a less efficient
recruitment process than the knob thorn population, which, although equally sparsely
represented by the very young classes, was very abundant (56%) within semi-mature to
mature classes, implying a solid and reproductively mature cohort.
Frequency of occurence
35
30
25
20
S.birrea
15
A.nigrescens
10
5
0
Classes 1-9 of basal circumference measurements in cm
Figure 3.8
Frequency distribution of ‘basal circumference classes’, comparing Sclerocarya
birrea and Acacia nigrescens populations - pooled across study sites.
3.5 Discussion
The quantification of elephant impact on vegetation structure and composition is best
approached on either a gradient of elephant density or the period of occupation (Barratt and
Hall-Martin, 1991). It is therefore crucial to acknowledge the divergent history of recent
elephant presence, with the period of elephant occupation being eight-fold longer on OWNR
than on JPNR, when interpreting similarities or differences between study sites. Steyn and
Stahlmans (2001) noted, however, that density alone might not be suitable as a measure for
expected impact outcomes. This suggestion found its explanation in the heterogenous nature
of savanna ecosystems, which, in addition to rainfall and water availability, was described as
driving elephant distribution patterns (Van Wyk and Fairall, 1969; Thomson, 1975; Steyn and
Stahlmans, 2001). However, due to the abundance of artificial water sources on both
reserves, the availability of surface water was not considered to be a driving factor in this
study. As indicated by the uneven distribution of species (low evenness values), preferred and
selected feeding patches should thus be distributed randomly and unevenly, resulting in
14
Chapter 3
concentrated and localized browsing pressure, potentially leaving niche refugia and recovery
windows for regeneration in less disturbed patches.
The increase in elephant densities, herbivory in general, human settlement, climate and fire
frequency, in combination or alone, have often been denounced as the ecological drivers for a
modification of habitat, notably for a decline in woodlands and for a considerable influence
on biodiversity, which may in turn have both positive or negative implications (e.g. MuellerDombois, 1972; Jacobs et al., 2002b; Makhabu et al., 2006; Kerley and Landman, 2006;
Lewis, 1987; Owen-Smith, 1988; Trollope et al., 1998; Van Wyk and Fairall, 1969; Lawton
et al., 1970; Cumming et al, 1997; Lock, 1993; Pringle, 2008; Owen-Smith, 1983; Lagendijk
et al., 2011; Waal et al., 2011). In the Queen Elizabeth National Park, Uganda, Field (1971)
found an annual decline in large trees of 14.6% with an elephant density of about 1.7km2,
while Lamprey et al. (1967) reported a yearly decline of 6% in the Serengeti National Park,
Tanzania with elephant densities as low as 0.135 per km2. A decline in the woody vegetation
layer was furthermore documented for the Serengeti-Mara woodlands by Dublin et al. (1990),
for Combretum/Terminalia woodlands in Burkina Faso by Jachmann and Croes (1991) and in
the Ruaha National Park by Barnes (1983), while Russel (1968) accordingly reported an
increase in grassland.
Because no fundamental, uncontrolled hot fire incidences have been recorded on either
reserve as far back as the time when records became available, elephants, herbivory and
climate (rainfall) seemed to be the relevant variables for the locations under investigation.
Although elephant utilization was identified as being capable of altering structure and
community of the woody vegetation layer, potentially causing shrub encroachment and
reduced tree density (Leuthold, 1970), these observations could not be soundly supported by
the results of this study as, with regard to vegetation structure and composition, the study site,
which has been exposed to elephants for approximately one decade (OWNR), did not
markedly differ from the one just recently (JPNR) exposed to elephants. The lack of such site
specific characteristics, as indicated by a comparable relative abundance of trees and shrubs
and by the similarity in the distribution of height classes, variables which were serving as
proxy for vegetation structure, therefore suggested that the accumulated impact over eight
years has had little consequence for the overall vegetation structure on OWNR. This implies
that either elephant densities on OWNR have been low and hence did not lead to significant
structural changes or that impact intensity has not been as severe as possibly assumed while
recovery took place. This argument was supported by the fact that diversity indices, which in
turn provided a proxy for vegetation community, did not markedly differ between study sites,
15
Chapter 3
thus implying that, up till now, the local floral biodiversity within OWNR has not been
negatively affected by elephant impact. Moreover, the relatively high species diversity ([1-D]
= 0.7), which was recorded for both study sites, implied a reduced vulnerability to a
potentially increasing level of disturbance. The assumed resilience was based on the fact that
biologically diverse communities generally allow for a better adaptation to habitat change as
they are more likely to include species which enhance ecosystem resistance and resilience
through the accumulation of species specific characteristics that vary in their responses to
change (Cleland, 2012). This implication is moreover gaining in importance as the selective
feeding ecology of elephants (Fowler and Mikota, 2006) might lead to the assumption that
preferred species must have declined in the face of elephant utilization for almost a decade,
while unpalatable species should have increased in numbers. The repeated hedging of
palatable species could be expected to result in a change of species structure and composition
in favour for less preferred species (Jachmann and Bell, 1985), with potentially ramifying
consequences for plant and animal species that depend on certain woodland characteristics
(Lombard et al., 2001). However, Jachmann and Bell (1985) also showed that the vegetation
community did not necessarily respond with an increase in unpalatable species when heavy
browsing was experienced. Augustin and McNaughton (1998) and Du Toit et al. (1990)
reported, furthermore, that tasteful and therefore often selected species could very effectively
react with compensatory growth.
The notion that the utilization of the woody vegetation layer by elephants was inevitably
followed by a more or less disastrous impact on biodiversity and vegetation structure
regularly seems to dominate the public discussion, thereby potentially biasing protective area
management. However, the feeding ecology of elephants and the resulting increase in
structural complexity were not only shown to support habitat creation for other species
(Pringle, 2008), to enhance gap formation, possibly benefitting plain game species in
particular, and to promote nutrient cycling (Owen-Smith, 1988), but were also found to
contribute to seed dispersal, notably of marula (Lewis, 1987). Results which reveal little
structural modification in areas utilized heavily and persistently (Jachmann and Croes, 1991;
Weyerhaeuser, 1995) tend to be lacking in the general controversy of the ‘elephant problem’.
It is therefore not clear to what extent these ‘positive effects’ are under-examined and underreported and our current comprehension of impact thus biased (Kerly et al., 2008). Moreover,
it has to be noted that, although the comparative evaluation of areas with and without
elephants may often be suitable, valuable and the only applicable way for a better
comprehension of the elephants’ feeding ecology and involved ecological processes and
16
Chapter 3
consequences, the frequent lack of historical baseline recordings on the vegetation for sites
like OWNR, however, might imply a potential ignorance of important facts on the site
specific evolution of habitats, which could provide essential explanations.
The availability distribution of height classes as recorded on both study sites holds additional
information which is of importance when discussing ‘elephant impact’ in Chapter 4 and 5.
Multiple studies identified the preferred feeding height of elephants as being roughly between
1m and 2.5m and above 5m (Croze, 1974 a,b; Greyling, 2004; Guy, 1976; Jachmann and
Bell, 1985; Owen-Smith, 1988; Pellew, 1983; Ruess and Halter, 1990; Smallie and
O’Connor, 2000). Therefore, if feeding habits of elephants on OWNR and JPNR were in
accordance with these findings, and because height classes four (1.5m - 2.0m), five (2.0m 2.5m) and ten (>4.5m) were found to be dominant with respect to the relative abundance,
elephant utilization would be in proportion to availability, indicating that height classes
sensitive to disturbance, i.e. browsing pressure, were not biased for exploitation and structural
modification was hence less likely to occur. Furthermore, the relatively high abundance of
large trees (>4.5m) on both reserves suggested that these, disregarding individual species,
have not suffered impact to an extent which, by leading to severe structural changes,
promoted the depletion of large trees. The fact that the total number of large trees on OWNR
was less than the total count on JPNR could probably be seen as a confounding variable,
resulting from different sample sizes between reserves, which were reflected by a reduced
total number of trees and shrubs on OWNR. The relatively low abundance of woody plant
individuals within the smallest height classes on both sites may reflect the impact on tree
recruitment of seedling predation by herbivore species in general such as impala (Aepyceros
melampus), buffalo (Syncerus caffer), and several antelope species, as well as by rodents
(Helm, 2011; Koppel et al., 1998; O’Kane et al., 2012; Prins and von der Jeugd, 1993; Waal
et al., 2011). This implication furthermore emphasized the necessity of considering the total
herbivore assemblage of an area, notably the interaction of meso- and megaherbivores, when
judging browsing effects on vegetation structure and community (Lagendijk et al., 2011).
The reason for a variation in the abundance of multi-stemmed and single-stemmed trees
between reserves was not self-evident but might at least partly be explained by historically
differing management regimes within the distinct areas and by land use practices, namely
cattle farming, which potentially could have taken place to diverging extents. Both reserves
are localized in an area that, prior to the time around the 1950s, was still predominantly
untamed, mostly lacking in fences or being individually fenced. Many of the individual farms
were historically agriculture holdings, mostly farming beef cattle, while others started early to
17
Chapter 3
operate as game or hunting farms. A gradual change from agricultural land use practices to
the focus on game was reported to have approximately taken place in the mid to late 1960s
and 1970s, when fences were erected and hence free roaming cattle excluded. Following this
change, individual properties on OWNR were mostly used as private holiday retreats or
hunting/game farms by 1970. JPNR evolved from the old Vienna Game Farm property,
which was developed as a Share Block with 32 shares being made available in 1987 and,
after purchasing the title deeds, started trading as Jejane Private Nature Reserve in 2003. The
lack of a common management plan for the distinct properties in the days before the
proclamation of the Nature Reserve status, as well as the fact that they joined the BNR and
the APNR at different times, led to individual management practices for activities such as
bush clearing and fire regimes, etc. (Jejane, 2013; Olifants West, 2013; Reinstorf, G., pers.
comm., 2013; Thomson, G., pers. comm., 2014), hence making it extremely difficult to
pinpoint the potential cause for the development of a site specific vegetation structure and
composition. Through all that time, before gaining Nature Reserve status, the veld had been
manipulated by bush thinning and tree removal in order to optimize grazing conditions.
Additionally, to minimize the loss of cattle, predator numbers were ‘managed’ (Olifants
West, 2013) and hence kept artificially low. Grazing pressure, initially by cattle, followed by
game, could potentially have trapped the vegetation in a rather open grassland state by
slowing down the process of seedling recruitment through trampling and seedling predation.
Unpalatable shrub and tree species as well as dominant species, resilient to browsing
pressure, potentially escaped this process and spread. Spinescent and multi-stemmed Acacia
species, which were described as woody encroacher species (Moleele and Perkins, 1998; van
Vegten, 1983), accounted for 16% (OWNR) and 22% (JPNR) of the multi-stemmed tree
species, potentially escaping browsing pressure due to characteristic defence mechanisms
(Elbanna, 2011; Rohner and Ward, 1997; Stapley, 1998). Combretum apiculatum (red
bushwillow), an early colonizer (Jordaan et al., 2004) and a community dominant species in
semi-arid savanna systems, which is frequently multi-stemmed and relatively resilient to
browsing pressure, comprised 57% and 56% of the multi-stemmed trees on OWNR and
JPNR, respectively. Nyengera and Sebata (2009) showed that, despite suffering extensive
structural impact, i.e. a reduction in height, when browsing pressure by high eland density
was at its maximum, Combretum apiculatum still continued to recruit into higher height
classes.
The high abundance of multi-stemmed trees could furthermore be explained by intense
herbivory from cattle and then by game, which was documented to often result in a change of
18
Chapter 3
stem morphology, converting single-stemmed individuals to multi-stemmed trees through
coppice once seedlings reached the height of 0.25m (Jacobs et al., 2002a). As fences have not
only excluded elephants but also predators in the early days, it is likely that herbivores
suffered little predation pressure, thereby resulting in a thriving browsing guild and hence
increased browsing pressure on the vegetation.
Waal et al. (2011) interestingly explained the lack of tree recruitment in areas with former
presence of cattle by the indirect effects of herbivores in combination with browsing pressure.
They showed that patches formerly used for cattle grazing, even 40 years after farming had
ceased, had high concentrations of inorganic nitrogen, potassium, calcium, and magnesium,
which enhanced plant production and supported high quality forage, resulting in the intense
grazing by game, which in turn lead to high rates of urine and dung deposition, further
promoting these nutrient hotspots. Plain game species in particular favoured these open
habitat patches for the high quality forage and the potential avoidance of predation,
furthermore accelerating the cycling of nutrients by dung and urine deposition. These fertile
conditions proved to constrain seedling recruitment of trees in favour of grasses and might
therefore be, in combination with the former suggestion that certain multi-stemmed species
have been resilient to such disturbances, a possible explanation for a relatively high
abundance of multi-stemmed species and the incoherent patch distribution of single-stemmed
tree stands in a location with a distinct history of cattle farming and browsing pressure.
A stable tree population could be assumed if alterations resulting from disturbance were
tolerated without the consequence of a severe population decline, thereby providing
continued support for ecosystem sustainability. For this population stability to become viable,
the distribution of relative age classes needs to allow for a successful recruitment rate.
Richards (1982/1983) suggested that the ideal distribution of relative age classes consisted of
40% juvenile trees, 30% semi-mature trees, 20% mature trees and only 10% senescent trees.
Such a relatively high abundance of juvenile trees would reflect a healthy recruitment of
seedlings and, in the face of potential stressors, enhance the chances of a good recruitment
rate into reproductively mature age classes, which would in turn secure the production of
seedlings. A different approach was offered by Lykke (1998), who defined a population that
supported a healthy recruitment process, and hence undergoes constant rejuvenation by
describing the given ‘reverse-J shape’ of the size class distribution. The recorded marula
populations of the study sites did not meet either of these definitions, indicating the lack of
such a robust and productive tree population.
19
Chapter 3
Based on Helm’s study (2011), which found that marula trees reached reproductive maturity
after approximately 46 years, populations on both study sites seemed to support a relatively
high proportion of reproductively mature trees, with JPNR’s population being generally
older. Yet, despite the presence of reproductive maturity and the therefore implied
availability of seeds, neither population came anywhere close to reaching the proposed 40%
for young trees. It was therefore essential to note that, although test results revealed a
statistically significant difference between the mean ages of the two marula populations, both
populations were characterized by a dominance of mature trees, indicating a long term
recruitment failure and hence implying a population structure prone to disturbance.
Owen-Smith (2007) identified the establishment of populations that consisted of
reproductively mature tree stands, but which lacked recruits, in the case where intervals
between recruitment events were long and variable, and suggested that such populations
would not reach a viable and stable size distribution. The under-representation of age classes,
including saplings and immature trees, has been recorded in various studies and was
described as the “phenomenon of a missing size class” (trees 2m - 8m in height and 5cm 40cm in diameter) (Gadd, 1997; Gadd, 2002; Helm, 2011; Jacobs et al., 2002a). Utilization
by elephants, seedling predation by herbivores and rodents, as well as fire suppression, have
been suggested as ecological drivers, leading to the creation of a recruitment bottleneck
(Haig, 1999; Helm, 2011; Helm and Witkowski, 2012; Jacobs et al., 2002a,b; Lewis, 1987;
Midgley and Bond, 2001). Instability, low recruitment and the lack of immature trees was
furthermore explained by Walker et al. (1986) who, in agreement with Owen-Smith (2007),
concluded that the successful regeneration of marula populations depended on episodic
recruitment events, which could be highly variable. The adult dominated populations
recorded on OWNR and JPNR indicate such a long-term recruitment deficiency. In
accordance with results documented by Helm (2011), who reported a recruitment failure on
JPNR for at least 60 years, this study recorded that the last successful mass recruitment
events seem to have taken place 60 to 130 years ago. In agreement with the observation that
the successful establishment of tree seedlings in savanna woodlands was episodic and only
occurred when conditions were favourable, as for example expressed by low browsing
pressure, the lack of fire suppression and high rainfall (Young and Lindsay, 1988), the
observed period of regeneration on OWNR and JPNR fell under such favourable conditions.
At the end of the nineteenth century, disease and hunting pressure led to an artificially low
number of mega- and mesoherbivores. The rinderpest epizootic was followed by a drastic
decline in ungulate populations (Carruthers et al., 2008; Kerley et al., 2008; Walker et al.,
20
Chapter 3
1986) and hunting virtually eliminated elephants in many parts of South Africa, with only
several individuals found between the Olifants and Letaba rivers (Stevenson-Hamilton,
1934). Because of the low numbers of browsing and grazing ungulates, pressure was
furthermore taken off the woody vegetation, as competition for grasses decreased, enabling
the remaining elephants to switch from browse to grass, a dietary component that, if
available, can make up to 70% of their wet season diet (Owen-Smith, 1988).
Consequently, as the successful recruitment of the populations under investigation took place
during a period of low browsing pressure and hence potentially little seedling predation,
herbivory and elephant utilization needed to be considered when discussing potential factors
that were and are driving marula population dynamics on both study sites. The effect of
seedling predation by elephants might, however, be moderate, as results of Chapter 4 showed
that elephants predominantly fed on plants above1m in height. Additionally, with regard to
elephant utilization, it has to be noticed that, according to Kettlitz (1962), prior to 1962 no
elephants were recorded outside of the KNP and, as both study sites erected fences
approximately around 1970, it can be assumed that elephant numbers in the area were
extremely low, if not zero, from the time of their near extirpation at the end of the nineteenth
century until the day fences were removed and the APNR joined. Therefore and because of
the lack of severe hot fires for a couples of decades (Reinstorf, G., pers. comm.; Thomson,
G., pers. comm.), the cause of little regeneration on OWNR and JPNR within the last 60
years might be explained by an increase in general browsing pressure following the recovery
of browser populations and by the episodic nature of recruitment recorded for marula
populations, rather than by elephant impact. These implications were once more in agreement
with Helm (2011), who concluded that regeneration was not seed limited, as indicated by the
abundance of reproductively mature trees, but predator limited (Crawley, 2000). Helm (2011)
recorded an extremely high level of seed and seedling predation (70%) for JPNR and referred
to high numbers of browsers (‘Many impala and other African browsers at high densities’
Thomson, G., pers. comm. in Helm, 2011) and rodents, which could presumably have thrived
due to low predation pressure and low fire frequency. The fact that, two years before the
JPNR was recolonized by elephants, Helm (2011) recorded similar results to those which
were found in this study, furthermore emphasizes the necessity of considering all possibly
involved variables that are potentially driving structure and composition of tree populations,
notably of marula, while stressing the importance of long-term studies and the evaluation of
results within a historical context.
21
Chapter 3
In summary, it can be stated that, at the time of this study, marula populations on both sites,
OWNR and JPNR, were dominated by adult trees, which are prone to natural senescence and
mortality. Moreover, seedling predation through herbivory and potential adult tree mortality
due to elephant utilization (Helm, 2011) may shape population dynamics on both reserves in
the future.
Marula and knob thorn have both been categorized as belonging to the most important
species in the KNP (Van Wyk and Fairall, 1969). As for marula, a decline in the Acacia
nigrescens population within the KNP has been reported (Moncrieff et al., 2008). Acacia
nigrescens was recorded as a species not only highly favoured and selected by elephants,
whose impact has been a cause of concern (Trollope et al., 1998), but also for its palatability,
especially following pruning events (Du Toit et al., 1990), as shown to be valid for the
majority of browser species (Fornara and Du Toit, 2008). Although having been exposed to
identical conditions as the marula population of OWNR and JPNR, the population structure
of Acacia nigrescens differed considerably. In comparison to the more bell-shaped to
negatively skewed population structure of marula, a positively skewed distribution was
recorded for the knob thorn population, both for the separate reserves as well as once records
were pooled across reserves. Assuming that basal circumference was positively correlated
with age, these patterns once more indicated an adult dominated population of marula versus
a more youthful age distribution pattern for the knob thorn population, implying recruitment
and hence better chances for species persistence. The resistance of Acacia nigrescens, a
species reported to be under chronic and heavy browsing pressure, was considered to have
naturally coevolved with high densities of browsers, therefore having adapted to high
browsing pressure through the development of physical defence mechanisms (Owen-Smith
and Cooper, 1987) and extremely effective compensatory growth abilities (Fornara and Du
Toit, 2008). The development of such defence strategies seemed to be lacking in the case of
Sclerocarya birrea and this, in addition to merely episodic recruitment events, resulted in an
incomparable vulnerability. Therefore, the maintenance of community dominance, despite
high disturbance levels, appeared to be possible for Acacia nigrescens but might be prone to
failure with regard to Sclerocarya birrea.
22
Chapter 3
3.6 Summarizing conclusion and implications
The first prediction was rejected as the overall structure and composition of the present
woody vegetation layer proved to be comparable between reserves. However, the lack of
baseline recordings that reflect the ‘natural state’ complicated the judgement of the current
vegetation state, as the degree of divergence from historical ecosystem conditions and
potential alterations over time could not be rated. As management and property owners of the
two study sites were concerned about a change in the recent and the current compositional
and structural diversity, the data recordings and results of this study could be used to identify
potential system change when compared with future surveys. Moreover, if endeavouring to
monitor the rate of change in the context of elephant utilization and without the image of
impact accumulation over several years, it is extremely valuable to monitor a site like JPNR,
which has only recently removed its fences.
The second prediction was rejected as considerable differences were found in the population
structure between Acacia nigrescens and Sclerocarya birrea. While Acacia nigrescens
seemed to be able to persist, Sclerocarya birrea might be prone to further decline. Population
structure and dynamics should therefore be carefully monitored, in order to advise potentially
crucial and adaptive management decisions.
Nevertheless, as no sound historical and therefore ‘natural’ benchmark records were available
for the study area, the missing knowledge of the ‘natural’ distribution pattern of marula
populations needs to be considered when discussing management options. This necessity
found its expression in Helm’s (2011) suggestion that the very likely extirpation of the JPNR
marula population in the future, although remaining an aesthetic concern, might be of no
ecological concern as this population became established due to artificially skewed and thus
favourable conditions (low browsing pressure, low elephant densities) in a habitat that under
presumably natural conditions (relatively low rainfall, chronic browsing pressure, higher
elephant densities and inadequate soil type) would not support this species. The marula
population on OWNR might face the same fate, as recorded distribution patterns were
identical.
The notion of a missing ‘natural yardstick’, which might lead to misinterpretation of the
currently observed population structure, may find additional support when comparing the
population structure of marula with the demographics of knob thorn (Acacia nigrescens), a
species that seemed to have coevolved with the local factors of disturbance through various
cycles of recruitment.
23
Chapter 3
In conclusion, it is inevitably crucial for management authorities, who wish to conduct
monitoring programmes which allow for an on-time detection of system change, to
acknowledge the lack of a ‘natural’ benchmark, as discussed above. The consideration of
whether savanna ecosystems are trapped in steady states or whether one should rather think
of a dynamic patch system, which constantly alternates and always has alternated between
varying vegetation states, notably woodland and grassland (CITES, 2002; Gillson, 2002)
should therefore be included in the adaptive management of Private Nature Reserves.
24
Chapter 3
3.7 References
Augustine, D.J., McNaughton, S.J (1998). Ungulate effects on the functional species
composition of plant communities: herbivore selectivity and plant tolerance Journal of
Wildlife Management. 62, pp. 1165-1183.
Augustine, D.J., McNaughton, S.J. (2004). Regulation of shrub dynamics by native browsing
ungulates on Eastern African rangeland Journal of Applied Ecology. 13, pp. 1325-1337.
Backeus, I. (1992). Distribution and vegetation dynamics of humid savannas in Africa and
Asia Journal of Vegetation Science. 3, pp. 345-356.
Barnes, R.F.W. (1983). The elephant problem in Ruaha National Park, Tanzania Biological
Conservation. 26, pp.127-148.
Barratt,D.G., Hall-Martin, A.J. (1991). The effects of indigenous herbivores on Valley
Bushveld in the Addo Elephant National Park. IN: Zacharias, P.J.K., Stuart-Hill,
G.C.(eds.) Proceedings of the first Valley Bushveld/Subtropical Thicket Symposium
Grassland Society of Southern Africa, Howick, pp. 14-16.
Begon, M., Townsend, C.R., Harper, J.L. (2006). Ecology. From individuals to ecosystems.
Oxford, UK: Blackwell Publishing.
Bond W.J., Smythe, K-A., Balfour, D.A. (2001). Acacia species turnover in space and time in
an African Savanna Journal of Biography. 28, pp. 117-128.
Bond, W.J., Loffell, D. (2003). Introduction of giraffe changes acacia distribution in a South
African savanna African Journal of Ecology. 39, pp. 286-294.
Carruthers, J., Boshoff, A., Slotow, R., Biggs, H.C., Avery, G., Matthews, W. (2008). The
elephant in South Africa: history and distribution In: Scholes, R.J., Mennell, K.G. (eds.)
Elephant Management - A Scientific Assessment for South Africa. Wits University
Press, Johannesburg, South Africa, pp. 23-83.
Caughley, G. (1980). Overpopulation In: Jewell, P.A., Holt, S., Hart, D. (eds.) Problems in
management of locally abundant wild mammals. Academic Press, New York, pp. 7-19.
CITES Briefing Document (2002). Ecological reality to cull (eds.) Gillson, L., Lindsay, K.
Cleland, E.E. (2012). Biodiversity and ecosystem stability Nature Education Knowledge.
3 (10): 14.
Colwell, R.K., Coddington, J.A. (1994). Estimating terrestrial biodiversity through
extrapolation Philosophical Transaction Royal Society London B. 345, pp. 101-118.
25
Chapter 3
Colwell, R.K. (2009). EstimateS. Statistical estimation of species richness and shared species
from
samples.
Version
8.2.
EstimateS
user’s
guide.
Available
from:
www.viceroy.eeb.uconn.edu/estimates [accessed December 19th, 2013].
Crawly, M.J. (2000). Seed predators and plant population dynamics. In: Fenner, M. (ed.) The
ecology of regeneration in plant communities. CABI publishing, London, 2nd edition,
pp. 167-182.
Croze, H. (1974a). The Seronera bull problem. I. The elephants East African Wildlife
Journal. 12, pp. 1-27.
Croze, H. (1974b). The Seronera bull problem. II. The tress East African Wildlife Journal.
12, pp. 29-47.
Cumming, D.H.M., Fenton, M.B., Rautenback, I.L., Taylor, R.D., Cumming, G.S.,
Cumming, M.S., Dunlop, J.M., Ford, G.S., Hovorka, M.D., Johnston, D.S., Kalcounis,
M.S., Mahlanga, Z., Portfors, C.V. (1997). Elephants, woodlands and biodiversity in
miombo woodland in southern Africa South African Journal of Science. 93, pp. 231236.
Dublin, H.T., Sinclair, A.R.E., McGlade, J. (1990). Elephants and fire as causes of multiple
stable states in the Serengeti-Mara woodlands Journal of Animal Ecology. 59, pp. 11471164.
Duffy, K.J., Os van, R., Vos, S, Aarde van, J., Ellish, G., Stretch, A-M.B. (2002). Estimating
impact of reintroduced elephant on trees in a small reserve South African Journal of
Wildlife. 32, pp. 23-29.
Du Toit, J.T., Bryant, J.P., Frisby, K. (1990). Regrowth and palatability of Acacia shoots
following pruning by African Savanna browsers Ecology. 71, pp. 140-154.
Elbanna, S.M. (2011). Ant-Acacia interaction: chemical or physical defense? Entomological
Research. 41 (4), pp. 135-141.
Field, C.R. (1971). Elephant ecology in the Queeen Elizabeth National Park, Uganda East
African Wildlife Journal. 9, pp. 99-123.
Field, C.R. (1976). The savannah ecology of Kidepo Valley National Park. II. Feeding
ecology of elephant and giraffe East African Wildlife Journal. 9, pp. 99-123.
Fornara, D.A., Du Toit, J.T. (2008). Community-level interactions between ungulate
browsers and woody plants in African savanna dominated by palatable-spinescent
Acacia trees Journal of Arid Environments. 72, pp. 534-45.
Fowler, J., Cohen, L., Jarvis, P. (1998). Practical Statistics for Field Biology. Chichester,
UK: John Wiley & Sons, 2nd edition .
26
Chapter 3
Fowler, M.E., Mikota, S.K. (2006). Biology, Medicine, and Surgery of Elephants. Fowler,
M.E., Mikota, S.K. (eds.). Blackwell Publishing, USA.
Gadd, M.E. (2002). The impact of elephants on the marula tree Sclerocarya birrea African
Journal of Ecology. 40 (4), pp. 328-336.
Gadd, M.E. (1997). Factors influencing the impact of elephants on woody vegetation in
private protected areas in South Africa’s lowveld. MSc Thesis, Faculty of Science,
University of the Wiwatersrand, Johannesburg, South Africa.
Gillson, L. (2002). Vegetation change in East African Habitat. PhD Thesis, University of
Oxford, UK.
Good, I.J. (1953). The population frequencies of species and the estimation of population
parameters Biometrika. 40, pp. 237-264.
Gotelli, N.J., Colwell, R.K. (2001). Quantifying biodiversity: procedures and pitfalls in the
measurement and comparison of species richness Ecology Letters. 4, pp. 379-391.
Grant, R.C., Kruger, L. (2007). The importance of large trees in a savannah – consequences
of their loss. Joint 42nd Annual Congress of the Grassland Society of Southern Africa &
4th Annual Meeting of the Thickets Forum, savannah and Woodland Dynamics I and II,
Tuesday 17 July 2007.
Greyling, M.D. (2004). Sex and age related distinctions in the feeding ecology of the African
elephant, Loxodonta africana. PhD Thesis, Faculty of Science, University of the
Wiwatersrand, Johannesburg, South Africa.
Guy, P.R. (1976). The feeding behaviour of elephant (Loxodonta Africana) in the Sengwa
area, Rhodesia. South African Journal of Wildlife Research. 6, pp. 55-63.
Haig, A.W. (1990). The impact of impala and elephant on the demography and dynamics of
Sclerocarya birrea subsp. caffra (Marula) in the eastern lowveld of South Africa.
Unpublished BSc thesis (Hons.) report, University of Natal, Pietermaritzburg, South
Africa.
Helm, C.V., Witkowski, E.T.F., Kruger, L., Hofmeyr, M., Owen-Smith, N. (2009). Mortality
and utilization of Sclerocarya birrea subsp. caffra between 2001 and 2008 in the
Kruger National Park, South Africa South African Journal of Botany. 75, pp. 474-484.
Helm, C.V. (2011). Investigating the life history strategy of an African savannah tree,
Sclerocarya birrea subsp. caffra (marula) PhD Thesis, Faculty of Science, University
of the Wiwatersrand, Johannesburg, South Africa.
27
Chapter 3
Helm, C.V., Wilson, G., Kruger, L., Midgley, J.J., Witkowski, E.T.F. (2011). Investigating
the vulnerability of an African savannah tree (Sclerocarya birrea spp. caffra) to fire
and herbivory Austral Ecology. 36 (8), pp. 964-97.
Helm, C.V., Witkowski, E.T.F. (2012). Characterizing wide spatial variation in population
size structure of a keystone African savanna tree Forest Ecology and Management.
263, pp. 175-188.
Henley, M. (2013). Vegetation and Questionnaire Report. Save the Elephants, Research and
Conservation, pp. 71.
Hortal, J., Borges, P.A.V., Gaspar, C. (2006). Evaluating the performance of species richness
estimators: sensitivity to sample grain size Journal of Animal Ecology. 75, pp. 274-287.
Horton, J.L., Hart, S.C. (1998). Hydraulic lift: a potentially important ecosystem process
Trends in Ecology and Evolution. 13 (6), pp. 232-235.
Humphries, C.J., Williams, P.H., Vanewright, R.I. (1995). Measuring Biodiversity Value for
Conservation Annual Review of Ecology and Systematics. 26, pp. 93-111.
Hurlbert, S.H. (1971). The non-concept of species diversity: a critique and alternative
parameters Ecology. 52, pp. 577-586.
Jacobs, O.S, Biggs, R. (2002a). The status and population structure of the marula in the
Kruger National Park South African Journal of Wildlife Research. 32, pp. 1-12.
Jacobs, O.S, Biggs, R. (2002b). The impact of the African elephant on marula trees in the
Kruger National Park South African Journal of Wildlife Research. 32, pp. 13-22.
Jachmann, H., Bell, R.H.V. (1985). Utilization by elephants of the Brachystegia woodlands in
the Kasungu National Park, Malawi African Journal of Ecology. 23, pp. 235-253.
Jachmann,
H.,
Croes,
T.
(1991).
Effects
of
browsing
by
elephants
on
the
Combretum/Terminalia woodland at the Nazinga Game Ranch, Burkina Faso, West
Africa Biological Conservation. 57, pp. 13-24.
Jejane (2013). History – timeline for Jejane Private Nature Reserve. Available from:
www.jejane.co.zza/tags/history [accessed 29th December 2013].
Jordaan, J.J., Wesels, D.C.J, Dannhauser, C.S., Rootman, G.T (2004). Secondary succession
in the Mopani veld of the Limpopo Valley, South Africa African Journal of Range and
Forage Science. 21 (3), pp.205-210.
Kemp, A.C., Beggs, K.S., Benn, G.A., Chadwick, P. (2003). A visual assessment of
vegetation structure for the Kruger National Park Koedoe. 40, pp. 117-123.
Kerley, G.I.H., Landmann, M. (2006). The impacts of elephant on biodiversity in the Eastern
Cape subtropical thickets South African Journal of Science. 102, pp. 395-402.
28
Chapter 3
Kerley, G.I.H., Landman, M., Kruger, L., Owen-Smith, N., Balfour, d., Boer de, W.F.,
Gaylard, A., Lindsay, K., Slotow, R. (2008). Effects of elephants on ecosystems and
biodiversity In: Scholes, R.J., Mennell, K.G. (eds.) Elephant Management - A Scientific
Assessment for South Africa. Wits University Press, Johannesburg, South Africa, pp.
146-205.
Kettlitz, W.K. (1962). The distribution of some of the large game mammals in the Transvaal
(excluding the Kruger National Park) Annals of the Cape Provincial Museum. 11, pp.
118-137.
Koppel van de, J., Prins, H.H.T (1998). The importance of herbivore interactions for the
dynamics of African savanna woodlands: an hypothesis Journal of Tropical Ecology.
14, pp. 565-576.
Lagendijk, D.D.G., Mackey, R.L., Page, B.R., Slotow, R. (2011). The effects of herbivory by
mega- and mesoherbivore on tree recruitment in sand forest, South Africa Plos One.
6 (3), pp. 1-9.
Lamprey, H.F., Glover, P.E., Turner, M.I., Bell, R.H.V. (1967). Invasion of the Serengti
National Park by elephants East African Wildlife Journal. 5, pp. 151-166.
Langevelde Van, F., Vijver van der, C.A.D.M., Kumar, L., Koppel Van der, J., Ridder de, N.,
Andel Van, A.,Sidmore, A.K., hearne, J.W., Stroosnijder, L., Bond, W.J.,Prins, H.T.,
Rietkerk, M. (2003). Effects of fire and herbivory on the stability of savannah
ecosystems Ecology. 84 (2), pp. 337-350.
Lawton, R.M. (1970). Elephants or fire-which is to blame? Oryx. 10, pp. 244-248.
Leuthold, W. (1970). Changes in tree populations of Tsavo East National Park, Kenya East
African Wildlife Journal. 15, pp.61-69.
Lewis, D.M. (1987). Fruiting patterns, seed germination, and distribution of Sclerocarya
caffra in an elephant-inhabited woodland Biotropica. 19, pp. 50-56.
Lombard, A.T., Johnson, C.F. Cowling, R.M., Pressey, R.L. (2001). Protecting plants from
elephants. Botanical reserve scenarios within the Addo Elephant National Park, South
Africa Biological Conservation. 102, pp. 191-203.
Lock, J.M. (1993). Vegetation change in Queen Elizabeth National Park, Uganda: 1970-1988
African Journal of Ecology. 31, pp. 106-117.
Ludwig, F., Kroon de, H., Prins, H.H.T. (2008). Impacts of savannah trees on forage quality
for a large African herbivore Oecologia. 155, pp. 487-496.
29
Chapter 3
Lykke, A.M. (1998). Assessment of species composition change in savannah vegetation by
means of woody plants’ size class distribution and local information Biodiversity and
Conservation. 7, pp. 1261-1275.
Magurran, A.E. (2004). Measuring Biological Diversity. Oxford, UK: Blackwell Publishing.
Makhabu, S., Skarpe, C., Hytteborn, H. (2006). Elephant impact on shoot distribution on
trees and on rebrowsing by smaller browsers ActaOecologica. 30, pp. 136-146.
McIntosh, R.P. (1967). An index of diversity and the relation of certain concepts to diversity
Ecology. 48, pp. 392-404.
McNaughton, S.J., Oesterheld, M., Frank, D.A., Williams, K.J. (1989). Ecosystem-level
pattern of primary productivity and herbivory in terrestrial habitats Nature. 341, pp.
142-144.
Midgley, J.J., Bond, W.J. (2001). A synthesis of demography of African Acacias Journal of
Tropical Ecology. 17, pp. 871-886.
Moleele, N.M., Perkins, J.S. (1998). Encroaching woody plant species and boreholes: is cattle
density the main driving factor in the Olifants Drift communal grazing lands, southeastern Botswana? Journal of Arid Environments. 40, pp. 245-253.
Moncrieff, G.R., Kruge, L.M., Midgley, J.J. (2008). Stem mortality of Acacia nigrescens
induced by the synergistic effects of elephants and fire in the Kruger National Park,
South Africa Journal of Tropical Ecology. 24, pp. 655-662.
Mueller-Dombois, D. (1972). Crown distortion and elephant distribution in the woody
vegetation of Ruhuna National Park., Ceylon Ecology. 53 (2), pp. 208-226.
Nyengera, R., Sebata, A. (2009). Effect of eland density and foraging on Combretum
apiculatum physiognomy in a semi-arid savannah African Journal of Ecology. 48 (1),
pp. 45-55.
O’Kane, C.A.J., Duffy, K., Page, B.R., Macdonal, D.W. (2012). Heavy impact on seedlings
by the impala suggests a central role in woodland dynamics Journal of Tropical
Ecology. 28, pp. 291-297.
Olifants West (2013). OWNR – Introduction and Background. Available from:
www.olifantswest.co.za [accessed 29th December 2013]
Owen-Smith, R.N. (1983). Dispersal and the dynamics of large herbivores in enclosed areas:
implications for management. In: Owen-Smith, R.N. (ed.) Management of large
mammals in African Conservation Areas. Haum Educational Publishers, Pretoria, South
Africa, pp. 127-143.
30
Chapter 3
Owen-Smith, R.N., Cooper S.M. (1987). Palatability of woody plants to browsing ruminants
in a South African savanna Ecology. 68, pp. 319-333.
Owen-Smith, R.N. (1988). Megaherbivores: the influence of very large body size on ecology.
Cambridge, Cambridge University Press.
Owen-Smith, R.N., Kerley, G.I.H., Page, B., Slotow, R., Van Aarde, R.J. (2006). A scientific
perspective on the management of elephants in the Kruger National Park and elsewhere
South African Journal of Science. 102, pp. 389-394.
Owen-Smith, R.N. (2007). Introduction to modelling in wildlife and resource conservation.
Blackwell Publishing, Oxford.
Pellew, R.A.P. (1983). The impacts of elephant, giraffe, and fire upon the acacia tortillis
woodlands of the Serengeti African Journal of Ecology. 21, pp. 41-74.
Pickett, S.T.A., Cadenasso, M.L., Benning, T.L. (2003). Biotic and abiotic variability as key
determinants of savanna heterogeneity at multiple spatiotemporal scales. In: Du Toit,
J.T., Rogers, K.H., Biggs, H.C. (eds.) The Kruger Experience: ecology and
management of savanna heterogeneity. Island Press, Washington.
Pringle, R.M. (2008). Elephants as agents of habitat creation for small vertebrates at the patch
scale Ecology. 89 (1), pp. 26-33.
Prins, H.H.T, Jeugd von der, D. (1993). Herbivore crashes and woodland structure in east
Africa Journal of Ecology. 81, pp. 305-314.
Richards, N.A. (1982/1983). Diversity and stability in a street tree population Urban
Ecology. 7, pp. 159-171.
Rogers, K.H., O’Keeffe, J. (2003). River heterogeneity: Ecosystem structure, function and
management. In: Du Toit, J.T., Rogers, K.H., Biggs, H.C. The Kruger Experience:
ecology and management of savanna heterogeneity. Island Press, Washington.
Rohner, C., Ward, D. (1997). Chemical and mechanical defence against herbivory in two
sympatric species of desert Acacia Journal of Vegetation Science. 8 (5), pp. 717-726.
Ruess, R.W., Halter, F.L. (1990). The impact of large herbivores on the Seronera woodlands,
Serengeti National Park, Tanzania African Journal of Ecology. 28, pp. 259-275.
Russel, E.W. (1968). The elephant problem in the Serengeti Oryx. 9 (6), pp. 404-406.
31
Chapter 3
Sankaran, M., Hanan, N.P. Scholes, R.J., Ratnam, J., Augustine, D.J., Cade, B.S., Gignoux,
J., Higgins, S.I., Le Roux, X., Ludwig, F., Ardo, J., Banyikwa, F., Bronn, A., Bucin, G.,
Caylor, K.K., Coughenour, M.B., Diouf, A., Ekaya, W., Feral, C.J., February, E.C.,
Frost, P.G.H., Hiernaux, P., Hrabar, H., Metzger, K.L., Prins, H.H.T., Sea,W., Tews, J.,
Worden, J., Zambatis, N. (2005). Determinants of woody cover in African savannas
Nature. 438, pp. 846-849.
Sankaran, M., Ratman, J., Hanan, N. (2008). Woody cover in African savannas: the role of
resources, fire and herbivory Ecology. 77, pp. 364-374.
Scholes, R.J., Archer, S.R. (1997). Tree-grass ratio interactions in savannas Annual Reviews
Ecology, Evolution, and Systematics. 28, pp. 517-544.
Senft, R.L., Coughenour, M.B., Baily, D.W., Rittenhouse, L.R., Sala, O.E., Swift, D.M.
(2005). Large herbivore foraging and ecological hierarchies BioScience. 37, pp. 789799.
Shackleton, S.E., Shackleton, C.M., Cunningham, T., Lombard, C., Sullivan, A.A.,
Netshiluvhi, T.R. (2002). Knowledge on Sclerocarya birrea subsp. caffra with
emphasis on its importance as a non-timber forest product in South and southern
Africa: a summary. Part 1: taxonomy, ecology and role in rural livelihoods Southern
African Forestry Journal. 194, pp. 27-41.
Shannon, C.E., Weaver, W. (1949). The mathematical theory communication. Urbaba, Il:
Unviersity of Illinois Press.
Shannon, G., Druce, D.J., Page, B.R., Eckhardt, H.C., Grant, R., Slotow, R. (2008). The
utilization of large savannah trees by elephant in southern Kruger National Park
Journal of Tropical Ecology. 24, pp. 281-289.
Simpson, E.H. (1949). Measurement of diversity Nature. 163, p. 688.
Smallie, J.J., O’Connor, T.G. (2000). Elephant utilization of Colophospermum mopane:
possible benefits of hedging African Journal of Ecology. 38, pp. 1-9.
Stapley, L. (1998).The interaction of thorns and symbiotic ants as effective defence
mechanism of swollen-thorn acacias Oecologia. 115, pp. 401-405.
Stevenson-Hamilton, J. (1934). Wild life in South Africa. Cassell, London, UK.
Steyn, A., Stahlmans, M. (2001). Selective habitat utilization and impact on vegetation by
African elephant within a heterogenous landscape Koedoe. 44, pp. 95-103.
Thomson, P.J. (1975). The role of elephants, fire and other agents in the decline of a
Brachystegia borhmii woodland Journal of the Southern African Wildlife Association.
5, pp.11-18.
32
Chapter 3
Trollope, W.S.W., Trollope, H.C., Biggs, D., Pienaar, D., Potgieter, A.L.F. (1998). Longterm changes in the woody vegetation of the Kruger National Park, with special
reference to the effects of elephants and fire Koedoe. 41 (2), pp. 103-112.
Van Vegten, J.A. (1983). Thornbush invasion in a savannah ecosystem in eastern Botswana.
Vegetatio. 56, pp. 3-7.
Van Wyk, P., Fairall, N. (1969). The influence of the African elephant on the vegetation of
the Kruger National Park Koedoe. 12, pp. 57-89.
Van Wyk, P. (1974). Trees of the Kruger National Park. Purnell, Cape Town.
Waal Van der, C., Kool, A., Meijer, S.S., Kohi, E., Heitkonig, I.M.A., Boer de, W. F.,
Langevelde Van, F., Grant, R.C., Peel, M.J.S., Slorow, R., Knegt de, H., Prins, H.H.T.,
Kroon de, H. (2011). Large herbivores may alter vegetation structure of semi-arid
savannas through soil nutrient mediation Oecologia. 165, pp. 1095-1107.
Walker, B.H., Stone, L., Henderson, L., Vernede, M. (1986). Size structure analysis of the
dominant trees in South Africa South African Journal of Botany. 52 (5), pp. 397-402.
Weyerhaeuser, F.J. (1995). Survey of elephant damage to baobabs in Tanzania’s Lake
Manyara National Park African Journal of Ecology. 23, pp. 235-243.
Wing, L.D., Buss, I.O. (1970). Elephants and forests Wildlife Monographs. 19, pp. 92.
Wynberg, R., Cribbins, J., Leakey, R., Lombard, C., Mander, M., Shackleton, S., Sullivan, C.
(2002). Knowledge on Sclerocarya birrea subsp. caffra with emphasis on its
importance as a non-timber forest product in South and southern Africa: a summary.
Part 2: Commercial use, tenure and policy, domestication, intellectual property rights
and benefit-sharing Southern African Forestry Journal. 196, pp. 67-78.
Young, T.P., Lindsay, W.K. (1988). Role of even aged populations in the disappearance of
Acacia xanthophloea woodlands African Journal of Ecology. 26, pp. 69-71.
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Chapter 4
Chapter 4
General Feeding Ecology of the African Elephant (Loxodonta africana)
4.1 Introduction
Elephants are mixed feeders with a diet that may range from grass to browse, comprising a
varied spectrum of plant species and plant parts, such as roots, bark, leaves, fruit and twigs
(Kerley et al., 2008). The browse-grass ratio of the diet may differ due to region, seasonal
changes, composition of soil nutrients and rainfall (Cerling et al., 1999; Field and Ross, 1976;
Owen-Smith, 1988). When available, grass mostly seemed to be preferred over browse
(Dublin, 1995). Grasses, as monocotyledons, generally contain more soluble carbohydrates
and fibre but lower contents of crude proteins, lipids, minerals and catabolizable energy than
dicotyledons, trees and shrubs, and are furthermore less digestible (Cameron et al., 1996).
The seasonal preference for grasses, despite these dietetic disadvantages, might result from
the high content of carbohydrates during the wet season and the generally low levels of toxic
substrates and tannins (Owen-Smith, 1988).
However, the proportional share of either
dietary component appeared to be strongly dependent on the season and on habitat
composition. While studies from Uganda, where grass was abundant, reported a high yearround consumption (Field 1971; Field and Ross, 1976), reports from the more wooded
savannas in eastern and southern Africa indicated a marked switch from grass to browse
according to season. While grass comprised most of the wet season diet, the dry season diet
was characterized by the increased utilization of woody species with the majority (70%-80%)
of the seasonal intake comprising woody material, roots and bark (Barnes, 1982; Guy, 1976).
It was suggested that the change in diet composition was based on seasonal changes in food
quality (Miller and Coe, 1993), which alternates in response to the phenology of plant
growth. Both grass and woody species provide maximum nutrients and minerals in the form
of new shoots and leaves in the early growing season. In the dry season, however, grasses
withdraw nutrients and leave only their dead leaves standing, while deciduous woody plants
translocate nutrients and carbohydrates away from senescent leaves before abscission, to
prepare for seasonal storage in non-photosynthetic tissue, like the inner bark tissue or the
roots (Owen-Smith, 1988). Therefore, in the dry season, when competition for resources
increases and animals are pressed for food, the African elephant, as a large-bodied herbivore,
has the ability to expand its diet, switching from grass to woody vegetation components,
potentially including a diet composed of more fibre and hence lower quality. Studies by
1
Chapter 4
Jarman (1968) and Bell (1971) provided a major scientific stepping stone to researching
questions on the relation of body size and its influential relevance to the digestive system.
The resulting ideas on the allometry of digestive anatomy and physiology were described as
the “Jarman-Bell-Principle” (Geist, 1974) and contributed profoundly to the understanding of
the digestive physiology of large herbivores and hence their dietary requirements. Jarman
(1974) and Bell (1971) reported that, while gut capacity remained a constant fraction of body
weight, mass-specific metabolic demands, i.e. the specific metabolic rate, decreased with an
increase in body weight, a phenomenon that was based on the relation between daily energy
requirements and metabolic body mass, BM0.75. These suggestions imply a digestive
advantage for large body sized herbivores, as the reduction in relative energy requirements,
together with an increase in total gastrointestinal capacity, appear to allow for the intake of
larger quantities, thereby compensating for a diet of lower quality (Clauss et al., 2003;
Demment and Van Soest, 1985; Parra, 1978). Fibrous diets of lower quality are processed
more slowly than less fibrous diets of high quality. Herbivores rely on a specialized
symbiotic gut microflora with the ability to digest the cell walls, which consist of pectin and
fibre. Such enzymatic processes take place in distinct anatomical fermentation chambers,
described as hindgut or foregut (Stevens et al., 1995) with the African elephant being a
hindgut fermenter. In contrast, ruminants, as foregut fermenters, are somewhat restricted in
their ability to tolerate highly fibrous diets because an increase in the fibre content inevitably
leads to a decrease in the turnover rate of the rumen as the food material needs to reach a
certain particle size before entering the gut system. Such a decrease in the passage rate might
hence lead to a failure in meeting the daily dietary requirements. These constraints do not
apply to a hindgut fermenter, such as the elephant, because of a comparably rapid passage
rate. The consequence of a less complete fermentation process, which results from an
accelerated passage rate can, however, be compensated by a higher food intake because of
this passage rate acceleration (Bell, 1971). Clauss et al. (2003) suggested that the design of
the gastrointestinal tract of hindgut fermenters enabled an adaptation for relative passage
acceleration, which proved to be essential, as the mean retention time (MRT) needed to be
long enough (Clauss et al., 2007) for the efficient microbial breakdown of fibre (lignin,
hemicellulose and cellulose) into its organic acid and carbohydrate components. This
suggestion was supported by Demment and Van Soest (1985), who recorded a gradual
prolongation of the MRT with an increase in body mass and hence an increase in absolute gut
capacity. Consequently they proposed that, with a certain body mass, hindgut fermenters
were able to attain the complete digestion of fibrous forage material.
2
Chapter 4
The seasonal pattern in diet, with a switch from grass to browse, implies an increase in
pressure on the woody vegetation during the dry season. Nevertheless, elephants are also
known to target a certain amount of grass during the dry season by digging and kicking
tussocks free from the soil and consuming leaf bases and roots, while discarding the remnants
(Field, 1971; Field and Ross, 1976). This increase in utilization of woody material was often
described as manifesting itself by the pollarding and toppling of whole trees and the structural
modification of canopies, as the elephant breaks off large branches (Balfour et al., 2007) to
access smaller plant parts, such as remaining foliage or fruits. As well as roots, bark was
described as a principal part of the late dry season diet (O’Connor et al., 2007), potentially
resulting in the structural and compositional change in vegetation that is caused by an
increase of such seasonal feeding habits (Holdo, 2003). The removal of bark can range from
ring-barking of large trees to bark-stripping of twigs by rotating small branches through the
mouth. Consequences for the plant are therefore dependent on the degree of impact (OwenSmith, 1988). The suggestion that bark is consumed for its phloem tissue, which was shown
to have high concentrations of carbohydrates, is well accepted. Bark was consistently selected
during the late dry season and in early spring just before leaf flush, when the phloem tissue
actively transports the sugar components to sites where they are needed to sustain growth
(Barnes, 1982; Chaney, 2003; Croze, 1974b, Field and Ross, 1976; Owen-Smith, 1988).
Elephants use their tusks to strip off the dead outer bark in order to gain access to the inner
bark (pers. observ.), which contains the living and carbohydrate-heavy phloem tissue and
comprises starch and nutrient storing cells. While total ring-barking may be detrimental for
the plant, bark-stripping events, which allow for some remaining phloem tissue and hence for
the translocation of nutrients, might well be followed by a process of healing and resprouting
(Buechner and Dawkins, 1961; Laws et al., 1975). The removal of bark could furthermore
potentially increase the trees’ susceptibility to an infestation with wood-boring insects and
fire (Barnes 1980; Weyerhauser, 1985). O’Connor et al. (2007) reported that the degree of
vulnerability was species specific, depending on the structure of the bark. They suggested
that tree species like Sclerocarya birrea, with bark that comes off in bigger chunks, were
more resistant to bark-stripping than tree species like Acacia, which were known for their
stringy bark.
Likewise roots, which were found to serve as storage organs for carbohydrates and nutrients,
appeared to be highly selected during the dry season (Chaney, 2003; O’Connor et al., 2007).
Variables that were identified to be decisive for a plant’s survival after toppling were the
dimension and extent of the root system, the strength of the wood, soil stability (O’Connor et
3
Chapter 4
al., 2007) and the proportional degree of the root system which remained intact (Croze,
1974b). The commonly available red bushwillow, Combretum apiculatum, for example, was
shown to resprout very productively if at least some roots remained unharmed (Eckhardt et
al., 2000). On the contrary, it was described that species like corkwood, Commiphora spp.,
generally shallow-rooted species, were extremely vulnerable to uprooting by elephants and
consequently suffered severe decline in some parts of the Ruaha National Park, Tanzania
(Barnes, 1985).
Foraging decisions can be made on different levels, varying from large spatial scales, such as
landscapes and habitats, to levels of plant species or plant part. As discussed above, body size
was recognized to be an essential factor that influences foraging decisions of mammalian
herbivores (Shrader et al., 2012). The lower mass-specific metabolic requirements, coupled
with a higher gut capacity, allow large body sized herbivores to consume high quantities of
low quality forage. From this fact arises the suggestion that selectivity for food scales with
body size, with a decreasing need for selectivity in large body sized herbivores (Bell, 1971;
Demment and Van Soest, 1985; Jarman 1974). Shrader et al. (2012) suggested that, due to
high absolute energy requirements and hence the need for abundant resources, large
herbivores would initially choose habitats with high food availability, regardless of species
composition, and only in consequence select the most palatable and nutritional species within
that very area. The ability to be less selective, while tolerating greater quantities of low
quality food, finds its maximum expression in the African elephant, itself the largest extant
terrestrial herbivore. Nevertheless, Bell (1971) emphasized that a high tolerance for low
quality food did not necessarily negate a preference for high quality resources. In agreement,
studies suggested that, although large scale choices (e.g. landscape and habitat) were
potentially made first, they would most probably be followed by fine scale decisions for
vegetation characteristics such as species composition and community structure (De Knegt et
al., 2011). To what degree different scales drive the selection of food might, however, vary
with season, with large scale choices predominating during the wet season when food is
abundant, while fine scale decisions (e.g. nutritional plant parts of high quality species) are
potentially of prime importance during the dry season, a time when animals are more likely to
experience nutritional stress. The theory of selective feeding habits of elephants on the plant
species level was supported by several studies, which analysed the interaction between
elephants and vegetation and concluded that feeding on woody species did not occur
indiscriminately. These results showed that elephants regularly preferred certain species,
which thereby constituted the majority of their diet, over other species that were equally or
4
Chapter 4
even more frequently available (Bax et al., 1963; Baxter, 2003; Chafota, 1999; Gadd, 1997;
Greyling, 2004; Guy, 1976; Owen-Smith, 1988), therefore indicating that feeding did not
necessarily occur in relation to species availability. Acacia spp., for example, were observed
to be a preferred source of browse for elephants during arid periods (Owen-Smith, 1988).
Although distinct species were preferred over others regardless of season, facts imply that the
dry season selection, compared to the plant species selection of the wet season diet, was
expanded in order to compensate for the seasonal limitation of resources. This suggestion was
confirmed by observations from the Chobe riverfront region in Botswana, where only three
shrub species accounted for 40% to 70% of the seasonal browse intake, with the range of
preferred species being somewhat more relaxed in the hot dry season (Chafota, 2007).
Furthermore, Kerley and Landman (2005) reported that elephants in the subtropical thickets
predominantly fed on a subset of 25 core species, accounting for 71% of the diet, despite the
potential availability of 146 species.
Selective herbivory was not only found to effect species composition by preferences for
particular woody species, but also to influence structural diversity with a distinct preference
for species of certain height classes. Several studies, for example, reported a preferred
feeding level between a height of 1m and 2m, while it was also recorded that size classes
between 2m and 4m were preferred by elephants. Others recorded that trees between 2m and
3m were mostly rejected, while trees of favoured species between 4m and 5m were found to
be a preferred target for toppling. It was furthermore suggested that these selectively pushed
over trees, when coppicing from the base, would contribute to an increased availability of
forage at an accessible and preferred feeding height (Caughley, 1976; Croze, 1974a; Gaylard
et al., 2005; Guy, 1976; Jachman et al., 1985; Owen-Smith, 1988).
Gaylard et al. (2005) emphasized the importance of monitoring both the utilization of plant
species as well as the impact on size classes. They suggested that mortality alone may lead to
a direct change in the compositional diversity, while the interaction of mortality and biomass
removal would indirectly drive a compositional change, by altering the distribution of size
classes, and directly contribute to changes in the structural diversity. Impact intensity on
individual woody species is therefore regulated by a combination of preferences and
selectivity for woody plant species and size classes (Buechner and Dawkins, 1961; OwenSmith, 1988; Thomson, 1975) and should hence be discussed in the context of availability, as
the depletion of certain species or size classes, i.e. a change in compositional or structural
diversity, seemed to be an inevitable function of abundance. Croze (1974b), for example,
reported that elephants in the Serengeti targeted size classes in relation to their availability, a
5
Chapter 4
result that implied less severity than if feeding had occurred inversely proportional to size
class availability. Furthermore, while several studies demonstrated the occurrence of woody
species utilization in proportional quantities to their availability (Barnes, 1983; Croze, 1974b;
Guy, 1976; Kalemera, 1998), Greyling (2004) found that the relatively high utilization of
aesthetically important species like Sclerocarya birrea or Lannea schweinfurthii, which were
found to be frequently accepted but only intermediately available, might be the cause of
increased concern to managers and landowners. These results furthermore emphasized the
importance of identifying species and height class availability, as this appears to be
indispensable for a meaningful quantification of impact in general and preferences in
particular.
4.1.1 Objectives
The objectives of Chapter 4 were to provide a better understanding of the late dry season diet
of elephants and their feeding habits, thereby evaluating mode and intensity of elephant
impact on vegetation structure and composition in general, while assessing availability and
acceptance of woody species and height classes and determining the utilization of different
plant parts in particular.
In view of the above literature review, both analysis and discussion were based on the
following assumptions and predictions:
The degree of damage to the woody vegetation layer varies between seasons but, due to time
constraints, only the late dry season vegetation impact was considered in this study as it was
expected to be the most severe at this time of the year. The utilization of monocotyledons
(grasses) was not tested. However, the dry season diet of elephants was predicted to
predominantly consist of plant material originating from woody species. This pressure on the
woody vegetation layer was expected to be recorded as intensified utilization. It was
furthermore predicted that the accumulated impact (’old’) would be of higher intensity on
OWNR, as the period of vegetation utilization by elephants was considerably longer than on
JPNR.
The bulk of the diet comprised only a selected subset of all available species. Although not
tested due to time constraints, it was furthermore assumed that the observed subset was more
relaxed compared to the wet season selection, when high quality forage was generally
abundant and selectivity not coupled with additional costs.
Plant species were not consumed in relation to their availability as certain species were
preferred over others, regardless of abundance.
6
Chapter 4
Foraging decisions were made on fine scales as resourses were limited. Selective feeding was
therefore observed on the plant species and plant part level. Due to the seasonal phenology of
deciduous woody plants and the resulting storage of nutrients in specific plant parts, roots and
bark were consumed in high proportions.
Distinct height classes were accepted and preferred over others.
4.2 Methods
Refer to Chapter 2 for a detailed description of the study area and the survey methods.
4.3 Data analysis
4.3.1 Woody plant species - availability and utilization
With the objective of providing a better understanding of the species availability and
utilization during the late dry season, only the ‘new’ data (backtracking and transects), which
allowed for an interpretation of the feeding ecology within a seasonal context, was analysed.
In order to account for site-specific characterisations, study sites were analysed separately in
a first step and pooled in a second step in the attempt to inflate sample sizes. To determine
which plant species were preferred over others in relation to their availability, acceptance and
availability indices were evaluated for each species, where acceptance was defined as the
reflection of the likelihood that an animal would commence feeding on a species which was
available close by (Owen-Smith and Cooper, 1987a, 1989). Site-based acceptability (SA)
indices were calculated by dividing the number of food plots where the species was utilized
by the number of plots in which this species was present. Availability indices (AI) were
calculated by dividing the number of food plots where a species was present by the total
number of plots that were surveyed. Acceptance and availability indices can range from 0 to
1.0 with the value 1.0 as the maximum, describing, in the case of acceptability, that the
species was utilized in all plots where it was present and, with regard to availability, that the
species was present in all plots that were surveyed.
In general, only species that were available at ten or more food plots and accepted in five or
more plots were chosen to be included for further statistical analyses. This decision was made
in order to identify the species which made up the bulk of the diet and to avoid the inclusion
of rare plants when discussing acceptability (Owen-Smith and Cooper, 1987a,b). This
resulted in a distinct group of eight core species, comprising Grewia spp. (raisin bush spp.),
Combretum apiculatum (red bushwillow), Acacia nigrescens (knob thorn), Acacia exuvialis
7
Chapter 4
(flaky thorn), Acacia erubescens (blue thorn), Commiphora spp. (corkwood spp.),
Dichrostachys cinerea (sickle bush) and Sclerocarya birrea (marula).
While Owen-Smith and Cooper (1985) called it general practice when species were regarded
as favoured if they were utilized in a higher proportion to their availability and regarded as
rejected when they were proportionally less abundant in the diet than in the habitat, they
proposed an additional definition in a later paper (Owen-Smith and Cooper, 1987a) that,
although more detailed, worked without the consideration of availability: a SA of <0.05
described neglected species, a SA between >0.1 and <0.25 defined species that were
intermediate in acceptability and a SA of >0.45 accounted for a species that was highly
favoured.
In this study, species were categorized as follows: Species were defined to be ‘highly
favoured’ if they were not frequently available (≤0.25) but well accepted (SA ≥0.4). Species
that were accepted in a higher proportion to their proportional availability within the habitat
but not categorized as ‘highly favoured’ were classified as ‘intermediately preferred’ and
species utilized less frequently than available were characterized as ‘less preferred’, while
species with an SA below 0.15 were referred to as ‘neglected’.
In order to assess whether and how plant species availability and acceptability were
correlated on either study site, the data was tested for normality and the Pearson correlation
test (rs) was applied. To establish whether species specific availability or species specific
acceptability significantly differed between study sites, a paired t-test was deployed for both
indices after the data had been tested for normality (Appendix C.1). In order to support the
results and to evaluate whether and how availability and acceptability indices respectively
were correlated between study sites, Pearson correlation tests (rs) were applied for availability
and acceptability indices respectively, thereby assessing whether woody species were equally
available and whether elephants on the two study sites fed on similar species. As test results
did not reveal a statistically significant difference between reserves, the data was pooled
across study sites and the relative dietary contributions of the eight woody species were
calculated by dividing the number of individual plants that were impacted within a species by
the total number of utilized woody plant individuals. With regard to the general elephant
feeding ecology, this process allowed for a sound insight into the relative dietary
contributions of the proportionally most utilized species during the late dry season.
8
Chapter 4
4.3.2 Utilization of trees and shrubs
‘New’ and ‘old’ impact data were treated separately, as ‘old’ impact accounted for the
accumulated impact over time, thereby possibly assigning utilization effects to elephants that
have potentially originated from other herbivores such as giraffe (Giraffa camelopardalis),
black rhino (Diceros bicornis), kudu (Tragelaphus strepsiceros) or impala (Aepyceros
melampus). This decision considered furthermore the site specific history of recent elephant
activity.
In order to find out whether it was justified to pool across sites when aiming for inflation of
sample sizes, chi-square (χ2) tests were conducted to assess the potential interaction between
the variables ‘location’ (OWNR or JPNR) and the ‘proportional utilization or negligence’ of
trees and shrubs respectively. As test results did not reveal any statistically significant
association between these variables, the data was pooled across study sites. In a second step,
chi-square (χ2) tests were applied to the pooled data for ‘old’ and ‘new’ impact separately, in
order to evaluate the potential interaction between the woody plant species category (tree or
shrub) and the utilization (utilized or not utilized), thereby determining whether the
utilization of trees differed from the usage of shrubs.
4.3.3 Acceptance and availability of height classes
Site-specific ‘height class acceptance ratios’ were calculated for trees and shrubs
respectively. ‘Old’ and ‘new’ impact data was once again analysed separately. Acceptance
ratios were calculated (Appendix C.2) by dividing the number of utilized trees or shrubs
within a height class by the total number of available trees or shrubs within that same height
class, therefore accounting for the proportional utilization of trees or shrubs within each
height class in relation to height class availability. Acceptance ratios ranged from 0 to 1.0,
with a value of 1.0 indicating that all available trees or shrubs in a specific height class had
been utilized.
After the data had been positively tested for normality, a paired T-test was applied for trees
and shrubs within paired height classes respectively, to assess whether acceptance ratios of
paired height classes differed between study sites. This procedure was applied to both ‘old’
and ‘new’ impact data.
Following the non-significant outcome, the ‘old’ and the ‘new’ data respectively were
pooled (Appendix C.3) and again tested for normality. To determine whether trees, compared
to shrubs, within distinct height classes were accepted differently in relation to their
availability, a paired t-test was applied to the ‘old’ impact data, while the Wilcoxon signed9
Chapter 4
rank test, the non-parametric equivalent, was applied to the ‘new’ data. Identical statistical
tools were used to evaluate potential differences in the acceptance ratios of trees and shrubs
respectively, comparing ‘new’ and ‘old’ impact data and thereby identifying whether trees or
shrubs within paired height classes were utilized differently, i.e. more frequently or less
frequently in a temporal context (accumulated impact versus fresh impact).
4.3.4 Impact modes, impact mode intensity and plant part utilization
Impact modes
Five different impact modes were identified: uprooting (ur), main stem breakage (ms),
breaking of larger (primary) branches in order to access smaller plant parts (bba), branch
breaking (bb) and bark-stripping (bs). Impact mode events per height class (hc) were counted
for each study site (Appendix C.4). ‘New’ data was again treated separately from ‘old’
data in order to allow for a distinct interpretation of the fresh impact compared to the
accumulated impact. It must be noted that ‘impact mode events’ did not equal frequencies of
impacted plant individuals, as a single woody plant could be utilized in more than one way,
for example first being bark-stripped and consequently uprooted. The frequency counts of
impact mode events do therefore not correspond with frequency counts of individual plants
from earlier chapters.
Sample sizes were relatively small and the use of statistical means therefore limited.
Descriptive statistics were predominantly used in order to allow for a sound interpretation of
the results.
To determine a potential association between the variables ‘location’ and ‘impact mode’, chisquare (χ2) tests were applied to ‘old’ and ‘new’ data records respectively. The sum of the
impact mode frequencies (Appendix C.5) across height classes had to be used, as values
within individual height classes were too small to account for a meaningful test result.
In a second step, ‘old’ and ‘new’ data was pooled separately across reserves (Appendix C.6
and C.7) in order to maximize sample sizes and hence allow for a valuable interpretation of
the general display of impact modes on a broader spatial scale. To assess whether recorded
frequencies of different impact mode events were associated with time, i.e. whether they
differed between accumulated impact data or ‘new’ impact data, a chi-square (χ2) test was
applied.
10
Chapter 4
Impact mode intensity
The calculation of impact mode intensity was based on impact mode recordings. For the
arguments discussed above, ‘old’ and ‘new’ impact data was once again treated separately.
Sample sizes and the resulting values were mostly too small to conduct any meaningful
statistical tests. Results and discussion were therefore predominantly based on descriptive
statistics.
The degree of intensity and with it the extent of impact was judged on the proportion of
removed biomass, whether this concerned roots, bark or branches. A removal of biomass
between 1% and 50% was defined as ‘light to moderate’ intensity, while a loss of >50% to
100% was represented as ‘heavy’ intensity. A further distinction was made, as ‘uprooting’
(ur) events and those that involved ‘main stem breakage’ (ms) were pooled and interpreted as
‘heavy structural change’. This distinction was made because all other impact modes, i.e.
‘breaking of larger branches to access smaller plant parts’ (bba), ‘branch breaking’ (bb) and
‘bark-stripping’ (bs) although potentially resulting in compositional or structural alterations
with time, were not expected to have comparatively imminent consequences for the vertical
vegetation structure and ultimately for vegetation composition.
Frequency counts for intensity values (Appendix C.8) were carried out for both study sites
and their proportional share was illustrated for ‘new’ and ‘old’ impact. Despite the absence of
statistical evidence for the lack of difference, the decision was made to pool ‘old’ and ‘new’
recordings across study sites, as the similarity between percentage shares of the two study
sites appeared reasonable. Finally, a chi-square (χ2) test was applied to assess whether there
was any interaction between the variables ‘time’ (accumulated or fresh impact) and ‘degree
of intensity’.
In order to get a better understanding of potential long-term effects of the accumulated
impact and hence its intensity, available impact recordings from the two vegetation transects
on OWNR for the late dry season in 2011 and 2012 were evaluated in combination with
results from this study.
11
Chapter 4
Plant part utilization
To evaluate in what proportion different plant parts contributed to the entire dry season diet,
the ingestion of roots, twigs and bark that followed uprooting, branch breaking and barkstripping events was analysed. All analyses concentrated exclusively on the ‘new’ impact
data as the end utilization of recorded ‘old’ impact mode events was often not
comprehensible and could furthermore not be interpreted within any seasonal context. As
main stem breakage (ms) and breaking of larger branches in order to access smaller plant
parts (bba) could have potentially resulted in the consumption of various plant parts, such as
the stem pith, twigs, bark or leaves with the end utilization therefore not definite, the
corresponding share of these impact modes was not further analysed but treated as ‘other’.
Because more than 90% of all recorded ‘branch breaking’ events which were noted for
Grewia spp. resulted in the utilization of the branch bark rather than in the consumption of
the entire twig, this proportion had to be separated from other bb events that resulted in the
actual consumption of the branch. The relatively thin branches of Grewia were frequently
observed to be broken off with the trunk and consequently bark-stripped by rolling and
drawing the twigs through the mouth. After successful removal, the bark was ingested and the
remnants of the twigs discarded (pers. observ.). That observation resulted in the decision to
analyse this Grewia cohort as being utilized for its bark and not for its branches, therefore
subtracting the corresponding count from the ‘branch’ unit and adding it to the ‘bark’ unit. It
was further decided to be of importance whether the consumed bark originated from the
debarking of trees or from the branch breaking of Grewia shrubs. The data was therefore
analysed separately as ‘bs grewia’ and ‘bs trees’.
The frequency distribution of plant part utilization was determined (Appendix C.9) and a chisquare (χ2) test was applied in order to assess a potential interaction between the variables
‘location’ and ‘plant part utilization’, thereby evaluating whether elephants on OWNR fed on
different plant parts than elephants on JPNR. The percentage share of the different plant parts
which made up the diet was illustrated for each study site. The lack of a significant
association between ‘location’ and ‘plant part utilization’ allowed for the pooling of the data
and the proportional shares of each plant part that comprised the diet were illustrated. The
utilization of plant parts was furthermore counted for each height class and the frequency
distribution demonstrated.
12
Chapter 4
4.4 Results
4.4.1 Woody plant species – availability and utilization
Of all available trees and shrubs that were sampled during the late dry season, 85% were
members of the eight defined ‘core’ species, while 95% of all impact events were recorded
for this cohort, implying that these species made up for the vast majority of the elephants diet.
A significant correlation between species availability and acceptability was neither
established for OWNR (Pearson correlation, rs = 0.401, P = 0.325) nor for JPNR (Pearson
correlation, rs = 0.223, P = 0.596). The correlation coefficients indicated a very modest
positive correlation for OWNR (rs = 0.401) and a weak positive correlation for JPNR (rs =
0.223), implying that not all plant species on either reserves were necessarily accepted in
relation to their availability. This result is reflected in Figure 4.1., as a strong positive
correlation would only be given if the species were accepted in perfect relation to their
availability, i.e. if they were all located close to the 45o line, indicating the ‘line of neutral
selection’ (Owen-Smith and Cooper, 1985).
13
Chapter 4
Acceptability index
(a)
1.00
0.90
0.80
0.70
0.60
0.50
0.40
0.30
0.20
0.10
0.00
Grew
Sbir
Capi
Dcin
Aeru
Anig
Comi
Aexu
0.00 0.10 0.20 0.30 0.40 0.50 0.60 0.70 0.80 0.90 1.00
Availability index
Acceptabilty index
(b)
1.00
0.90
0.80
0.70
0.60
0.50
0.40
0.30
0.20
0.10
0.00
Sbir
Aeru
Anig
Grew
Dcin
Comi
Capi
Aexu
0.00 0.10 0.20 0.30 0.40 0.50 0.60 0.70 0.80 0.90 1.00
Availability index
Figure 4.1
Acceptance and availability indices for woody species that were available at ten or
more food plots on OWNR (a) and JPNR (b).
Preferred spp. = red; intermediately preferred spp. = yellow; less preferred spp. = light green;
neglected = blue
Sbir=Sclerocarya birrea, Aeru=Acacia erubescens, Anig=Acacia nigrescens,
Dcin=Dichrostachy cinerea, Comi=Commiphora spp., Aexu=Acacia exuvialis,
Capi=Combretum apiculatum, Grew=Grewia spp.
14
Chapter 4
Sclerocarya birrea was ‘highly favoured’ on both study sites (AI = 0.16; 0.11 and SA = 0.9;
0.6). Additional ‘highly preferred’ species consisted of Acacia erubescens (AI = 0.21, SA =
0.46) and Dichrostachys cinerea (AI = 0.25, SA = 0.53) on OWNR and of Acacia erubescens
(AI= 0.16, SA = 0.53) on JPNR. While Acacia nigrescens (AI=0.31, SA=0.37) was
‘intermedieately preferred’ on OWNR, Combretum apiculatum (AI=0.63, SA=0.54), Grewia
spp. (AI=0.85, SA=0.79) and Commiphora spp. (AI = 0.55, SA = 0.35) were proportionally
more available in the environment than the extent to which they were utilized and thus fell
under the category of ‘less preferred’. Acacia exuvialis (AI = 0.26, SA = 0.21), Commiphora
spp. (AI = 0.32, SA = 0.30) Combretum apiculatum (AI = 0.58, SA = 0.33) and Grewia spp.
(AI = 0.89, SA = 0.63).were ‘less preferred’ on JPNR as they were proportionally more
available in the environment than the extent to which they were utilized, while Acacia
nigrescens (AI = 0.24, SA = 0.32) and Dichrostachys cinerea (AI = 0.38, SA = 0.49) were
‘intermediately preferred’. Grewia spp., although recorded to be proportionally less abundant
in the diet than available in the habitat, were still the most utilized species (Figure 4.3).
Although site specific characteristics could be implied from the interpretation of Figure 4.1,
neither differences in species specific availability (paired t-test, t = 0.17, n = 8, P = 0.87) nor
in acceptability (paired t-test, t = 0.12, n = 8, P = 0.27) of the eight core species proved to be
statistically significant between reserves. These results were furthermore supported by a
significant and strong positive correlation between availability indices (Pearson correlation,
rs = 0.874, n = 8, P = 0.005) and between acceptability indices (Pearson correlation, rs =
0.876, n = 8, P = 0.004) of the study sites, which indicated not only that woody species were
available to a similar extent but also that elephants on both OWNR and JPNR had similar
preferences. The pooling of the data across sites was thus found to be justified.
The pooled data was illustrated in Figure 4.2 and indicated that, while Sclerocarya birrea
(AI = 0.14, SA = 0.76) and Acacia erubescens (AI = 0.2, SA = 0.42) could be regarded as
‘highly favoured’ species, Dichrostacys cinerea (AI = 0.3, SA = 0.45) and Acacia nigrescens
(AI = 0.25, SA = 0.33) were identified as ‘intermediately preferred’ species, and Acacia
exuvialis (AI=0.17, SA=0.15), Grewia spp. (AI=0.89, SA=0.67), Combretum apiculatum
(AI=0.55, SA=0.30), and Commiphora spp. (AI=0.36, SA=0.26) were shown to be
proportionally less utilized in relation to their availability within the habitat. Acacia exuvialis
was on the threshold of being regarded as ‘neglected’.
15
Acceptability indices
Chapter 4
1.00
0.90
0.80
0.70
0.60
0.50
0.40
0.30
0.20
0.10
0.00
Sbir
Grew
Aeru Dcin
Anig
Comi
Aexu
Capi
0.00 0.10 0.20 0.30 0.40 0.50 0.60 0.70 0.80 0.90 1.00
Availability indices
Figure 4.2
Acceptance and availability indices for woody species that were available at ten or
more food plots - pooled across study sites.
Preferred spp. = red; intermediately preferred spp. = yellow; less preferred spp. = light green;
Sbir=Sclerocarya birrea, Aeru=Acacia erubescens, Anig=Acacia nigrescens,
Dcin=Dichrostachy cinerea, Comi=Commiphora spp., Aexu=Acacia exuvialis,
Capi=Combretum apiculatum, Grew=Grewia spp.
As illustrated in Figure 4.3, 95% of the elephants’ diet was made up of the eight woody core
species. Grewia spp. became the principal food source by constituting 54% of the diet.
Acacia
nigrescens (10)
4%
11 spp. (13) 5%
with each
constituting
<1%
Acacia exuvialis
(6) 2%
Sclerocarya
birrea (15) 6%
Acacia
erubescens
(12) 5%
Commiphora
spp. (11)
4%
Dichrostachys
cinerea (28) Combretum
11%
apiculatum
(22) 9%
Figure 4.3
Grewia spp.
(139)
54%
Relative dietary contributions of the eight woody plant species that were commonly
utilized by elephants during the late dry season - study sites combined. The value in
brackets shows the number of utilized individuals within each species.
16
Chapter 4
Although readily consumed in comparison with other species, the acceptability index (SA =
0.62) of Grewia spp. was considerably lower in relation to the species availability (AI = 0.89)
in the environment, which excluded these species from the definition of ‘staple food’.
According to Petrides (1975), Grewia spp. were therefore classified as ‘principal foods’,
defining food that is consumed in the greatest quantities regardless of being preferred or less
preferred. Dichrostachys cinerea (11%) and Combretum apiculatum (9%) comprised the
second biggest component of the recorded elephant diet while Sclerocarya birrea (6%) and
Acacia erubescens (5%) were found to be in third place. A total of 10% of all utilization
events was recorded for the three species: Commiphora spp. (4%), Acacia nigrescens (4%)
and Acacia exuvialis (2%). Only 5% of the total impact affected the remaining 17 available
species, of which each was available in less than ten plots and of which 11 were utilized less
than five times, with none of them constituting more than 1% of the diet.
4.4.2 Utilization of trees and shrubs
No interaction between the variables ‘location’ and ‘utilization or negligence of shrubs’ was
found. This result applied to the ‘old’ (chi-square test, χ2 = 0.176, df = 1, P = 0.675) and to
the ‘new’ data (chi-square test, χ2 = 3.415, df = 1, P = 0.065). Similar insignificant test results
were achieved when assessing the potential interaction between the variables ‘location’ and
the ‘proportional utilization of trees’. Neither variables of the ‘old’ impact data (chi-square
test, χ2 = 0.302, df = 1, P = 0.583) nor those of the ‘new’ impact data (chi-square test, χ2 =
3.205, df = 1, P = 0.073) were found to be significantly associated. These results were used to
justify the pooling of the data across reserves, as illustrated in Fig 4.4.
(a)
(b)
20%
80%
Percentage of utilized plants
Percentage of utilized plants
100%
22%
utilized
60%
40%
80%
78%
20%
not
utilized
0%
trees
shrubs
80%
42%
60%
utilized
60%
40%
20%
58%
40%
not
utilized
(a)
0%
trees
shrubs
Woody plant category
Woody plant category
Figure 4.4
100%
Proportional utilization and negligence of trees and shrubs - pooled across study sites,
for ‘new’ impact data (a) and for ‘old’ impact data (b).
17
Chapter 4
No significant association was found between the proportion of utilization and the woody
plant category (tree or shrub) for the ‘new’ impact data after records had been pooled across
study sites (chi-square test, χ2 = 0.637, df = 1, P = 0.425). However, test results for the ‘old’
impact data, after pooling, proved to be statistically highly significant (chi-square test, χ2 =
41.528, df = 1, P = <0.001), suggesting that the proportion of utilized versus not utilized
woody plants was not independent of whether this utilization concerned trees or shrubs,
implying an association in that shrubs were proportionally more impacted than trees.
4.4.3 Acceptance and availability of height classes
Test results (Appendix C.1) neither indicated a significant difference between the site specific
acceptance ratios of trees and shrubs within paired height classes for ‘new’ impact
(paired t-test, n = 10, t = 1.07, P = 0.312; t = 1.88, P = 0.093) nor for ‘old’ impact
(paired t-test, n = 10, t = 0.64, P = 0.540; t = 1.22, P = 0.255). This indicated lack of
significant site specific characteristics allowed for the pooling of data across study sites for
‘old’ and ‘new’ impact separately.
Trees compared to shrubs within paired height classes were accepted in similar proportions
(Wilcoxon signed-rank test, W = 39, n = 10, P = 0.889) in relation to their availability with
regard to the ‘new’ impact data. However, with regard to the accumulated (‘old’) impact data,
trees compared to shrubs within matched height classes were utilized differently (paired ttest, t = 7.27, n = 10, P = <0.001) in relation to their availability. This test result was
furthermore emphasized by the significant difference (paired t-test, t = 17.81, n = 10, P =
<0.001) in shrub utilization when comparing ‘new’ with ‘old’ data records, implying that, in
relation to their availability, shrubs were utilized in a considerably higher proportion with
regard to ‘old’ impact data. In contrast, no significant difference (Wilcoxon signed-rank test,
W = 47.5, n = 10, P = 0.982) between acceptance ratios of trees was found when comparing
‘old’ with ‘new’ data, indicating that the accumulative effect on trees was not as profound as
the accumulative impact on shrubs.
The distribution of height class availability and acceptance (Figure 4.5) differed between
trees and shrubs, with a disproportionally high availability of shrubs between 1m and 2.5m,
while trees above 4.5m in height were disproportionally highly available compared to trees of
other height classes. The remaining trees showed a relatively equal spread across height
classes between 0.5m and 4.5m with a very low presence below 0.5m.
Shrubs were
moderately present between 0.5m and 1m, between 3m and 4m and rare above 4m and below
0.5m
18
Chapter 4
(a)
(b)
400
400
350
350
Frequencies
Frequencies
300
100%
availability
250
200
150
no. of utilized
trees
100
50
300
200
150
no. of utilized
shrubs
100
50
0
0
Height classes 1-10, in metres
Height classes 1-10, in metres
(c)
(d)
400
350
100%
availability
300
Frequencies
Frequencies
100%
availability
250
250
200
no. of utilized
trees
150
100
50
0
Height classes1-10, in metres
Figure 4.5
400
350
300
250
200
150
100
50
0
100%
availability
no. of utilized
shrubs
Height classes 1-10, in metres
Distribution of impact frequencies, illustrating the proportion of utilized trees (a),(c)
and shrubs (b),(d) within different height classes in relation to their 100%
availability for ‘old’ (a),(b) impact data and for ‘new’ (c),(d) impact data - pooled
across study sites.
The distribution pattern of impact frequencies for trees and shrubs respectively remained the
same over time with certain height classes preferred over others. Overall utilization of trees
and shrubs was distributed in relation to availability with the most abundant height classes
hence impacted the most, implying a utilization of trees and shrubs within distinct height
classes in relation to availability of these height classes. This pattern was equally relevant for
‘new’ and for ‘old’ impact, indicating that although utilization was more profound with
regard to the accumulated impact data, the most impacted height classes were still the most
abundant ones. In summary, trees above 4.5m in height and shrubs between 1m and 2.5m
were favoured over other height classes.
19
Chapter 4
4.4.4 Impact modes, impact mode intensity and plant part utilization
Impact modes
No interaction between the variable ‘impact mode frequencies’ and ‘location’ was found for
either ‘new’ impact (chi-square test, χ2 = 4.252, df = 4, P = 0.373) or for ‘old’ impact
(chi-square test, χ2 = 2.777, df = 4, P = 0.596), implying that the variables ‘location’ and
‘occurrence of impact mode’ were independent of each other, which was well reflected by the
similarity of proportionate shares of impact modes (Table 4.1).
Table 4.1
Percentage share of impact mode events for ‘new’ (a) impact data and ‘old’ (b)
impact data.
ur=uprooting, ms=mainstem breakage, bba=breaking of larger branches in order to
access smaller plant parts, bb=branch breaking, bs=bark-stripping
(a)
OWNR
ur
ms
bba
bb
bs
20%
5%
7%
64%
4%
(b)
JPNR
OWNR
22%
9%
3%
59%
7%
ur
ms
bba
bb
bs
5%
4%
9%
77%
5%
JPNR
6%
5%
6%
79%
4%
Figure 4.6 illustrates the frequency distribution of impact mode events across height classes
for the two study sites. ‘Branch breaking’ (bb) was by far the most prolific impact mode for
‘new’ and ‘old’ records respectively and regardless of location. The majority of these mode
events fell into height classes 3, 4, 5 and 6, ranging from 1m-3m. Branch breaking events for
‘old’ impact were observed up to fifteen times more frequently than any of the other impact
modes. While these ‘bb’ mode events still comprised the most frequently recorded impact
mode with regard to ‘new’ impact, they only amounted to a third of ‘old’ bb events. Barkstripping (bs), main stem breakage (ms) and the breaking of larger branches in order to access
smaller plant parts (bba) were recorded slightly more often with regard to ‘old’ impact, while
total counts of uprooting events were observed marginally more frequently with respect to
‘new’ impact. Woody species within the ‘new’ impact data seemed to be uprooted throughout
height classes 3 to 10 (1m to >4.5m) without following any obvious pattern, while ur events
for the ‘old’ impact also occurred throughout height classes 3 to 10 but were heavily
weighted towards taller species. No impact modes were recorded within the smallest height
classes (0-1m) for ‘new’ impact, while several branch breaking (bb) events in height class 1
20
Chapter 4
and two main stem breakages (ms) in height class 2 were recorded with regard to ‘old’
impact. Bs and bba events were skewed towards taller woody plants, regardless of impact
age.
(b)
90
80
70
60
50
40
30
20
10
0
Impact mode frequencies
Impact mode frequencies
(a)
bs
bb
bba
ms
ur
90
80
70
60
50
40
30
20
10
0
Height classes 1-10, in metres
Impact mode frequencies
Impact mode frequencies
bba
ms
ur
(d)
bs
bb
bba
ms
ur
90
80
70
60
50
40
30
20
10
0
bs
bb
bba
ms
ur
Height classes 1-10, in metres
Height classes 1-10, in metres
Figure 4.6
bb
Height classes 1-10, in metres
(c)
90
80
70
60
50
40
30
20
10
0
bs
Frequency distribution of impact mode events across height classes for OWNR ‘new’
(a), JPNR ‘new’ (b), OWNR ‘old’ (c), JPNR ‘old’ (d).
ur=uprooting, ms=mainstem breakage, bba=breaking of larger branches in order to
access smaller plant parts, bb=branch breaking, bs=bark-stripping
21
Chapter 4
The association between the variables ‘time’ and ‘impact mode frequency’ proved to be
highly significant (chi-square test, χ2 = 61.752, df = 4, P = <0.001). This result implied an
interaction between the occurrence of impact modes and the period of time in which they had
accumulated, therefore indicating the necessity of differentiating between the recordings of
fresh impact versus the recordings of accumulative impact, as illustrated in Figure 4.7.
(a)
6% (60)
(b)
3% (45)
2% (21)
1% (14)
18%
(176)
2% (18)
4% (55)
ur
ur
45%
(615)
ms
ms
bba
bba
43%
(594)
bb
70%
(676)
2% (32)
bb
bs
bs
none
none
2% (33)
Figure 4.7
Proportions of impact mode events and non-impact events for ‘new’ (a) and ‘old’ (b)
impact data with the frequency values in brackets - pooled across study sites.
ur=uprooting, ms=mainstem breakage, bba=breaking of larger branches in order to
access smaller plant parts, bb=branch breaking, bs=bark-stripping
While ms and bs events occurred in equal proportions when comparing ‘old’ and ‘new’ data
records, ur events were recorded in a slightly higher proportion for ‘new’ impact and bba
events had a marginally higher proportionate share with regard to ‘old’ impact data.
However, although the total counts of ‘non-impact’ events hardly differed (676 versus 615),
the proportional share did (70% versus 45%). The high share (70%) of ‘non-impact’ events
for ‘new’ impact recordings was accompanied by a relatively low proportion (18%) of bb
events. In contrast, the comparatively low share (45%) of ‘non-impact’ events for the ‘old’
data was accompanied by a relatively high proportion (43%) of bb events. The results seemed
therefore to be driven by differences in the frequency of bb events.
22
Chapter 4
Impact mode intensity
Although sample sizes between study sites differed markedly in part, the proportional share
for distinct impact mode intensities (Table 4.2) did not diverge to any extreme extent when
comparing OWNR with JPNR, neither for ‘new’ nor for ‘old’ impact.
Table 4.2
Degrees of intensity and their proportional share for ‘new’ and ‘old’ impact
- study sites compared.
ur=uprooting, ms=mainstem breakage, bba=breaking of larger branches in order to
access smaller plant parts, bb=branch breaking, bs=bark-stripping
impact intensity
(ur+ms) heavy structural
change
(bba) heavy intensity
(bba) light to moderate
intensity
(bb) heavy intensity
(bb)light to moderate
intensity
(bs) heavy intensity
(bs) light to moderate
intensity
No Impact
new impact
% OWNR % JPNR
old impact
% OWNR % JPNR
8%
1%
9%
1%
5%
1%
6%
1%
2%
8%
0%
4%
4%
8%
2%
6%
11%
1%
14%
2%
35%
1%
38%
0%
1%
70%
1%
70%
1%
45%
2%
44%
The biggest proportionate share of any intensity class was represented by ‘branch breaking’
(bb) events with light to moderate consequences, followed by heavy structural change that
resulted from ‘uprooting’ (ur) and ‘main stem breakage’ (ms) events, and by branch breaking
(bb) events of heavy intensity. The breaking of larger branches in order to access smaller
plant parts (bba) and the feeding mode of bark-stripping (bs) resulted in very similar low
proportions of light to moderate and heavy intensity respectively. This overall pattern was
observed for both ‘new’ and ‘old’ impact recordings. However, the proportional shares of
branch breaking (bb) with light to moderate intensity and the percentage unit of ‘no impact’
seemed to differ markedly, depending on the nature of ‘time’, i.e. accumulated versus new
impact.
This impression was confirmed when the pooled data (OWNR + JPNR) was tested for a
potential association between the variables ‘time’ and ‘impact mode intensity’ and the test
results (chi-square test, χ2 = 221.141, df = 7, P = <0.001) indicated a highly significant
interaction between impact mode intensity and the period of time within which they had
accumulated, as illustrated in Figure 4.8 below.
23
Chapter 4
(a)
8% (ur+ms)
1% (bba)
6% (bb)
1% (bs)
(ur+ms) heavy
structural change
(bba) heavy
(bb) heavy
(bs) heavy
1% (bba)
13% (bb)
1% (bs)
70%
(no impact)
(bba) light to
moderate
(bb) light to
moderate
(bs) light to
moderate
None
(b)
6% (ur+ms)
45%
(no impact)
1% (bba)
7% (bb)
(ur+ms) heavy
structural change
(bba) heavy
1% (bs)
(bb) heavy
3% (bba)
(bs) heavy
36% (bb)
(bba) light to
moderate
(bb) light to
moderate
(bs) light to
moderate
None
2% (bs)
Figure 4.8
Degrees of intensity and their proportional share for ‘new’ (a) and ‘old’ (b) impact
- pooled across study sites.
ur=uprooting, ms=mainstem breakage, bba=breaking of larger branches in order to
access smaller plant parts, bb=branch breaking, bs=bark-stripping
Although the overall pattern described above continues over time, ‘branch breaking’ (bb)
events of light to moderate intensity were recorded three times more frequently for the
accumulated impact (36% versus 13%), while the proportional share of other impact mode
intensities remained relatively consistent. Furthermore, it was noted that ‘heavy structural
change’ through toppling and pollarding was observed slightly more often with regard to
‘new’ impact (8% versus 6%), while the breaking of bigger branches (bba) that resulted in a
removal of biomass between 1% and 50% was recorded slightly more frequently with regard
to ‘old’ impact (3% versus 1%). In summary, it can be said that, with the exception of ‘light
to moderate intense’ bb events, with its proportional share of the ‘old’ recordings being
24
Chapter 4
threefold that of the ‘new’ recordings, the distribution of impact mode intensities and their
percentage share remained relatively constant over time.
Table 4.3 illustrates the ‘newly’ recorded impact modes and their intensity, which were
sampled on the OWNR transects during the late dry season over a period of 3 years. No
impact events were recorded for the year 2012. One case of main stem breakage and one
uprooting event were reported over a three year period, both in 2013. The ms event was
documented for a buffalo-thorn (Ziziphus mucronata) of 2.5m in height while the uprooting
event was observed for a Grewia sp. of 3.5m in height. Over the period of three years, ‘new’
branch breaking events of light to moderate intensity made up the majority (67%) of all
recorded utilization modes, while only one bb event resulted in a loss of biomass over 50%.
One bark-stripping event and four bba events had a removal of biomass below 50% as a
consequence.
Table 4.3
Frequency distribution of impact modes and their intensity that was recorded on
the two vegetation transects on OWNR during the late dry season (August,
September, October) over a period of 3 years.
ur=uprooting, ms=mainstem breakage, bba=breaking of larger branches in order to
access smaller plant parts, bb=branch breaking, bs=bark-stripping
Aug-11
Aug-12
Aug-13
Sep-11
Sep-12
Sep-13
Oct-11
Oct-12
Oct-13
Sum over 3yrs
Long-term monitoring records for transect 1&2 on OWNR
ms ur
bba (≤50%)
bb (≤50%)
bb (>50%)
0
0
2
5
0
0
0
0
0
0
0
0
0
1
0
0
0
0
0
0
0
0
0
0
0
1
0
0
3
0
0
0
2
1
0
0
0
0
0
0
0
1
0
6
1
1
1
4
16
1
bs (≤50%)
1
0
0
0
0
0
0
1
Plant part utilization
No association (chi-square test, χ2 = 2.208, df = 4, P = 0.698) was found between the
variables ‘location’ and ‘utilization of plant parts’, implying no distinct differences in the late
dry season diet between study sites with regard to the consumption of the various plant parts.
This result justified the pooling of data. The lack of site specific characteristics was
furthermore well reflected by the similarity of proportional shares (Table 4.4) when
comparing the outcome of the pooled data with the results that were obtained for each study
site separately.
25
Chapter 4
Table 4.4
Proportional share of plant part utilization within the late dry season, comparing
values between ‘study sites pooled’ with ‘OWNR’ and ‘JPNR’.
roots
21%
19%
22%
sites pooled
OWNR
JPNR
branches
29%
27%
30%
bark
total
38%
40%
37%
bark
grewia
32%
36%
30%
bark
trees
6%
4%
7%
other
12%
12%
12%
As reflected by Figure 4.9, which illustrates the proportional share of plant part utilization,
the utilization of bark (38%) comprised most of the late dry season diet. However, only 6% of
the consumed bark originated from bark-stripping events of trees, while 32% resulted from
the utilization of Grewia branches. The second largest share of the diet (29%) was made up
of branches, while the utilization of roots amounted to 21% and the remaining 12% of the
diet comprised unidentified (‘other’) plant parts.
12%
(35)
21%
(60)
roots
branches
32%
(93)
bark trees
bark grewia
other
29%
(83)
6%
(18)
Figure 4.9
Proportional share of plant part utilization within the late dry season diet, with the
frequency values in brackets - study sites pooled.
branches=small branches that were consumed partly or entirely
bark grewia=broken branches of grewia spp. that were exclusively used for their bark
bark trees=utilization of bark following bark-stripping mode events of larger trees
bark total=bark grewia combined with bark trees
other=unidentified utilization of plant parts following ms or bba events
26
Chapter 4
As reflected by Figure 4.10, which demonstrates the frequency distribution of utilized plant
parts across distinct height classes, none of the plant parts which were recorded as being part
of the late dry season diet of elephants within the study area came from woody species below
1m in height. Smaller branches, which were consumed partly or entirely, originated from
woody species across height classes 3 to 10, with the principal share being broken off trees
above 4.5m. Bark that was obtained through classical bark-stripping events on trees
originated almost exclusively (15 times out of 18) from trees above 4.5m, while bark that
came from the utilization of Grewia branches predominantly came from shrubs between 1.5m
and 3.5m in height. The roots that were accessed for consumption stemmed from similar
shares of woody species across height classes 3 to 10.
60
50
Frequencies
bark trees
40
bark grewia
30
20
branches
10
roots
0
0-0.5 0.5- 1- 1.5- 2- 2.5- 3- 3.5- 4- >4.5
1 1.5 2 2.5 3 3.5 4 4.5
Height classes 1-10, in metres
Figure 4.10
Frequency distribution of utilized plant parts across height classes 1-10.
27
Chapter 4
4.5 Discussion
While results supported findings from Chapter 3 (diversity indices) by indicating that habitat
composition (species availability) between study sites was comparable, they illustrated that
elephants on the two reserves fed on similar species during the late dry season. Of all 30
species that were sampled during the study period, a narrow range of eight species (27% of
all available species) comprised 95% of elephant diet, of which certain ones were preferred to
others, thereby implying selective foraging decisions on the plant species level. Greyling
(2004) recorded an equally narrow range of six to eight species, which made up 70-80% of
the entire dry season diet. Of the eight species on OWNR and JPNR, Grewia spp. constituted
the bulk of the entire dietary intake (54%). Results are harmonious with the findings of
Greyling (2004), who recorded a comparatively high relative dietary contribution of Grewia
spp. for bull groups (38%) and family units (41%) in the dry season. Chafota (1999) reported
that elephants predominantly subsisted on shrub species in general, regardless of season.
The study results are furthermore in accordance with those of Owen-Smith and Cafota
(2012), who found that elephants in northern Botswana fed selectively on a small subset of
available woody plant species, while the majority of total encountered species was either
neglected or mostly rejected. Their observation that 40-70% of the total food intake during
each season was constituted by only one or two shrub species, which were readily available
and favoured, was furthermore supported by the observation that Grewia spp. on OWNR and
JPNR made up the highest dietary intake while being the most abundant species.
Owen-Smith and Chafota (2012) demonstrated an increase in the number of moderately to
highly accepted woody species during the dry season when compared with the wet season
selection, which indicated that the narrow dry season selection of woody plant species
recorded on OWNR and JPNR would be even smaller during the wet season, thereby
potentially implying a generally moderate to low pressure on the overall assemblage of the
woody vegetation layer. Nevertheless, the concentrated utilization of a narrow range of
species could evoke the expectation of intense exploitation of these species, a concern that
might currently however not be entirely valid for this study area, as the majority of the most
utilized species on both study sites was either consumed in more or less close relation to their
availability within the habitat or proportionally less utilized in relation to availability.
Furthermore, the distinct plant species which made up the majority of the diet and the degree
of their utilization should be evaluated in a context that allows for an interpretation of their
ecological role. For example, apart from Grewia spp., which were recorded as being
28
Chapter 4
abundantly available and aesthetically of little value (Greyling, 2004), Dichrostachys cinerea,
a species that was considered to be a key species associated with bush encroachment (Shrader
et al., 2012), made up the second largest share of the diet, implying that ecological concern
should not inevitably be coupled with the utilization of woody plant species, and accentuating
the importance of quantification on the plant species level.
However, the results also indicated that ‘species of concern’ (Sclerocarya birrea, Acacia
nigrescens) were very well accepted but not abundantly available and that they together
contributed 10% to the late dry season diet, which furthermore implies that the quantification
of impact on the plant species level in general and species specific monitor programmes in
particular are of importance.
The utilization of trees and shrubs was noticeably higher with regard to ‘old’ impact, which
clearly illustrated the accumulative nature of impact over time, therefore indicating the
necessity of separating ‘old’ and ‘new’ impact in situations where the cause of utilization
cannot be clearly assigned. The proportional utilization of shrubs was significantly higher
than the impact on trees, with the total number of utilized shrubs (469) being almost twice as
high as the number of impacted trees (241). These results supported the suggestion that other
variables have to be considered where woody plant utilization cannot be confidently assigned
to elephants, and hence indicate the importance of taking the entire browser guild into
account. While elephants and giraffes (Giraffa camelopardalis) are the only browser species
that are able to reach into higher canopy trees, various smaller browsers and mixed feeders,
such as kudu (Tragelaphus strepsiceos), impala (Aepycerus melampus) or black rhino
(Diceros bicornis), which feed at shrub height, need to be considered when interpreting the
outcome of accumulated impact on shrubs over time. Wiseman et al. (2004) reported that
mesoherbivores on the Ithala Game Reserve had utilized the vegetation with an impact
approximately three times as high as the effect of elephants. Browsing habits of these species
could either lead to the initial hedging of shrubs that have not yet been impacted by elephants
(Lewis, 1987; Mosugelo et al., 2002; Pellew, 1983; Prins and von der Jeugd, 1993; Ruess and
Halter, 1990) or they might prevent shrubs once utilized by elephants from recovering and
consequently from regenerating into adult height classes (Field and Ross, 1976; Lewis,
1991), therefore potentially having a severe influence on the vertical structure of the shrub
layer.
The above discussion on the accumulative nature of the ‘old’ impact data over time was well
reflected and supported by the findings that, regardless of woody plant category (tree or
shrub), overall impact frequencies across height classes were higher for ‘old’ than for ‘new’
29
Chapter 4
impact data. For shrubs, these distinct height classes comprised the range between 1m and
2.5m, additionally supporting the suggestion that other browser species, which feed at shrub
height, needed to be considered when evaluating accumulative impact over time (O’Kane et
al., 2011). In agreement with earlier studies (Croze, 1974b; Kalemera, 1989), height classes
seemed to be utilized in quantities proportional to their availability, implying reduced
pressure on less abundant height classes. This study found that shrub species of 1m to 2.5m in
height and tree species of >4.5m were utilized most, thereby supporting the results of several
earlier studies, which found the preferred feeding height to be between 1m and 2m, with the
height of the utilized plants being slightly greater (Caughley, 1976; Guy, 1976; Jachmann and
Bell, 1985; Ruess and Halter, 1990; Smallie and O’Connor, 2000). In harmony with other
findings, results indicated that woody species below 1m in height, even when identified as the
preferred food source, were largely ignored (Caughley, 1976; Croze, 1974b; Pellew, 1983).
Accordingly, Gadd (1997) noted that seedlings, although the most available size class within
the corresponding study area, were accepted less in than 0.2% of all feeding bouts. Some
studies reported that larger trees were only utilized when smaller height classes were not
available (Caughley, 1976; Laws et al., 1975), while others found that their utilization
decreased with increasing height and concerned mostly activities such as bark-stripping or
uprooting (Croze, 1974a; Jacobs, 2001; Smallie and O’Connor, 2000). In the face of growing
concern for a potential decline in large trees it is therefore crucial that, in addition to the
quantification on the plant species level, the distribution of height classes, their availability
and acceptance have to be considered in order to evaluate system condition.
The infrequent utilization of the very small height classes should prove to be beneficial for
the recruitment process, while repeated feeding and pruning of woody plants of intermediate
heights, although the most abundant, might however trap the vegetation in a distinct state
dominated by these height classes. The repeated use of preferred species and height classes, a
pattern that was termed ‘hedging’ (Styles, 1993), might be explained by the observation that
elephants showed distinct preferences for previously utilized woody plants (Anderson and
Walker, 1974; Jachmann and Bell, 1985; Lewis, 1991). Smallie and O’Connor (2000), for
example, reported a selective preference for previously utilized Colophospermum mopane
and suggested that, due to coppice growth, previously utilized trees provided more branches
of the preferred size for consumption than those which had not been impacted earlier. These
suggestions could have a positive implication for other browsers, as the repeated utilization of
shrubs and trees by elephants and the resulting coppice growth might lead to an increase in
browse availability and palatability for herbivores in general, thereby creating distinct
30
Chapter 4
‘browsing lawns’ that potentially evolve into nutrient and resource-rich environments
(Cromsigt and Kuijper, 2011). These considerations were furthermore emphasized by the
disproportionally large amount of small branch breaking (bb) events between 1m and 3m. As
the preferred feeding height for browsers was found to be about neck height (Du Toit, 1990;
Makhabu, 2005), it might be suggested that this height interval matched the feeding height of
various mesoherbivores. Results that indicated a disproportionally large unit of light to
moderate intensity for branch browsing events furthermore implied the utilization by smaller
herbivores. The effects of browsing by mesoherbivores ought therefore not to be
underestimated, as these browsing species could affect the vegetation structure by either
initiating the damage or controlling the state of structural change which originated from prior
utilization by megaherbivores, i.e. elephants (Lagendijk et al., 2011). Such interspecific
facilitation, the creation of ‘browsing lawns’ (Hulbert and Andersen, 2001; Makhabu et al.,
2006; Van Koppel et al., 1998) and the resulting potential ‘feeding loops’ (Bergqvist et al.,
2003; Du Toit et al., 1990) have to be considered when analysing accumulative impact over
time.
The comparable use of resources and habitat within the two study sites, which was previously
indicated by a similar utilization of species and height classes, was furthermore supported by
the lack of distinct site specific characteristics with regard to impact mode and the utilization
of plant parts. However, the likeness between the recordings of impact mode intensity with
regard to ‘old’ impact rather indicated a diverging utilization of the two study sites in terms
of intensity, as it was expected that, with respect to the distinct histories of elephant
occupancy, the vegetation of OWNR would have been impacted more intensely over the
years than that of JPNR. Hence, it is implied that within the recent past, i.e. since JPNR
dropped its fences in 2013, utilization of the habitat by elephants was more intense on JPNR
than on OWNR. This implication might find its explanation in reports by Peel (2012) and
Spencer (2010), who recorded a rise in elephant numbers within areas where fences had just
been removed, hence indicating initial exploration of newly opened habitats, in particular by
young adult ‘pilot bulls’ that had not yet established their musth cycle, and indicating the
‘dispersal sink’ effect (Lidicker, 1975; Owen-Smith, 1983; Spencer, 2010; Van Aarde and
Jackson, 2007). Moreover, if this peak of habitat exploration was followed by a decrease in
the initial influx of newly exploring elephants (Peel, 2012; Spencer, 2010), a future decline in
elephant numbers, and therefore impact intensity, could potentially be expected for JPNR,
just as it was observed for OWNR.
31
Chapter 4
The consideration of a decrease in intense and concentrated habitat utilization could be
supported by sampling data for the two OWNR transects over a period of three years, which
indicated very little ‘new impact’ caused by elephants, with time. Results might furthermore
indicate that, over time, recovery from initial and intense utilization can potentially take place
to such an extent that accumulated impact intensity is moderated in relation to its initial
degree.
More uprooting events were observed as ‘new’ impact than were recorded during baseline
sampling that accounted for the accumulated impact. These ‘new’ ur events furthermore
spread throughout the majority of height classes, while ‘old’ ur events were mostly recorded
as having impacted large trees. Because the sampling took place during the late dry season,
most deciduous woody species were dormant with only a few old leaves, fruit or berries
available, while grasses were generally nutrient-poor. Competition between trophic guilds
increases the pressure on woody species during the dry season, due to overlapping forage
preferences (Lagendijk et al., 2011). Available browse at commonly accessible feeding
heights (1m-3m) therefore potentially becomes depleted at first, while foliage or fruit that
was left in the higher canopy might still be available. In order to reach these resources as well
as to access the roots, which like the bark were identified as the plants’ translocation and
storage organs for nutrients and minerals during the dry season (Bloom et al., 1985; OwenSmith and Cooper, 1989), elephants use their ability to uproot and push over trees (Croze,
1974b; Jachman et al., 1985). The fact that the bulk of ur events was recorded for ‘new’
impact might be seen as a result of the different survey techniques. While the ‘new’ impact
data reflected a seasonal context, the ‘old’ data simply accounted for a once-off recording of
accumulated impact over time. Although resource acquisition generally seemed to be the
prime cause of tree felling, uprooting of large trees was also found to serve social rank
purposes between dominant bull elephants (Guy, 1976; Lamprey et al., 1967), which in
general are more likely to fell mature trees due to a body mass almost double that of a cow
(Owen-Smith, 1988). However, Croze (1974a) argued that resource access was still assumed
to be the major cause of uprooting events, as it was often found to be the younger bulls of
lower rank that felled large trees. Nevertheless, during the dry season all demographics are
dependent on nutrients that are stored in the root system, therefore potentially leading to an
increase in uprooting events by cows and sub-adults throughout all height classes, including
smaller shrubs. In addition, elephants were observed to consume entire root systems and
often the entire plant after uprooting these shrubs (pers. observ.), which in turn might have
32
Chapter 4
been the cause of predominantly recording ur events on large trees, of which either the dead
trunk or the resprouting remnants remained in place, with regard to ‘old’ impact.
The breakage of main stems (ms) and that of larger branches (bba) was observed (pers.
observ.) to be utilized for the consumption of smaller and otherwise inaccessible plant parts,
such as twigs, leaves and berries within canopy height, therefore probably and
predominantly recorded within larger height classes. Tree felling events (ms) were little
recorded in proportion to other impact mode events. Chafota and Owen-Smith (2009)
reported that the few tree felling events that were recorded in their study occurred in areas
where forage in the shrub layer was not sufficiently available, indicacting that resources at
this height were still ample during this study. Owen-Smith (1988) described the ecological
benefits that could potentially result from the toppling and pollarding of large trees. It was not
only predicted that the release of nutrients from the woody tissue, after felling, was
accelerated via decomposition but additionally suggested that, while mature phases of
vegetation generally had slower turnover rates, pioneer phases with fast growth rates, which
potentially succeeded after such disturbance, contained higher concentrations of nutrients,
therefore leading to ecological succession and providing an increase in forage availability for
other herbivores.
Barnes (1982) reported that roots, bark and wood comprised 70% to 80% of the dry season
diet, which agrees with the findings of this study that 80% of the consumed plant parts during
the sampling period consisted of roots, bark and branches, thereby supporting the general
tenor that these plant materials supply most of the dietary bulk under dry conditions
(Greyling, 2004; Guy, 1976; Owen-Smith, 1988). The relatively large proportion (38%) of
consumed bark could potentially be well explained by the endeavour to gain the high contents
of carbohydrates that flow through the phloem tissue in the late dry and early spring season
when this tissue seems to be most active, just before the beginning of flower and leaf
production (Barnes, 1982; Owen-Smith, 1988). It was concluded that classical bark-stripping
(bs) events were principally more common on large trees, not only for practical reasons (the
use of proportionally large tusks as specialized feeding tools) but also for a potentially larger
gain, hence the predominant recording for taller height classes. Chaney (2003) reported that
young twigs and smaller branches were preferred by animals that targeted the bark for its
sugary phloem tissue, because the relative proportion of the nutrient laden inner bark
compared to the outer layer was higher and hence the amount of unpalatable components
such as lignin and phenols lower, which could thereby explain the relatively high proportion
of utilized Grewia branches. Because the feeding habit of bark-stripping on large trees was
33
Chapter 4
found to be of major concern to managers of protected areas (Ruess and Halter, 1990), it
seemed important to quantify bark utilization in terms of its origin. Therefore it has to be
emphasized that only 6% of the entire diet and 14% of all bark utilization consisted of bark
that was obtained through bark-stripping of trees above 4.5m. Of these bark-stripping events,
two thirds resulted in a loss of biomass over 50%. This relatively high intensity could
possibly be explained within the seasonal context. In contrast, only one fifth of all barkstripping events that were recorded for large trees with respect to the accumulative impact
was recorded as ‘heavy’, implying that prolific bark-stripping might in general either result in
healing and regeneration or die-off, and was therefore comparatively rarely recorded with
regard to ‘old’ impact. Most of the consumed bark (86% of all bark utilization) originated
from branches of Grewia spp. that were recorded to be highly available within the study area.
Hence, one may suggest that the relatively large share of bark utilization in the late dry
season was not as detrimental for the woody vegetation layer as possibly suspected.
Furthermore, as this type (‘grewia bark’) of plant part utilization was based on branch
breaking events, it could potentially result in a pruning effect that would be followed by a
production of new shoots and thereby, although affecting the vertical structure of the
vegetation, not threaten the survival of the plants but rather enhance resource productivity at
shrub height.
4.6 Summarizing conclusion and implications
In agreement with the underlying predictions, it was found that the bulk of the elephant diet
within the two study sites consisted of a narrow range of eight woody species. Furthermore,
supporting the expectation that elephants in general, regardless of sex, fed selectively and that
foraging decisions were made on the plant species level, it was observed that of these eight
‘core species’ certain were preferred, a few consumed in close relation to their availability,
while others were less preferred. From this it follows that although the majority of the ‘core
species’ were utilized in close relation to their availability or in relatively lower proportions
with regard to their availability, the vulnerability of certain preferred species might yet
increase if these species are already a cause for concern (e.g. Sclerocarya birrea). The
implementation of species specific monitoring programmes over the long term should
therefore be considered and it should become general practice to quantify the dietary
components in terms of species composition and vegetation structure rather than basing the
judgement solely on structural variables such as size class or growth form (Kerley et al.,
34
Chapter 4
2008). The prediction that foraging decisions were made on the plant part level was
confirmed in that 59% of the late dry season diet consisted of bark and roots, indicating the
selection of nutrient containing plant parts.
Trees above 4.5m and shrubs between 1m and 2.5m seemed to be the preferred target for
utilization, implying a preferred feeding height below 2.5m and a preferred utilization of
large trees, most likely for activities such as bark-stripping, uprooting and the breaking of
large branches in order to access smaller plant parts. Additionally, study results showed that,
speaking in relative terms, shrubs and trees within distinct height classes were either utilized
in lower proportions to their availability or in relation to their occurrence within these height
classes. This implies that, although the vegetation structure was impacted, speaking in total
terms, the structural diversity, i.e. the distribution of size classes, did not appear to shift from
the current shape.
Although elephants were not excluded on either study site, their temporal occupancy differed
markedly (8yrs versus <1yr). Hence, the intensity of accumulated impact and therefore the
degree of change in compositional and structural diversity was expected to diverge extremely
between reserves, a potential inference which was not confirmed by the results of this study.
It is recommended that the monitoring and evaluation of elephant impact, although remaining
site specific, should not be conducted in isolation from neighbouring areas, but rather on
larger spatial and temporal scales that allow for comparison between areas in order to get a
better understanding of the broader range of consequences. It is, moreover, essential to make
the distinction between cumulative and recent impact if attempting to assess the rate of
induced habitat alteration, as well as to consider results within a seasonal context. Authorities
of protective areas that are faced with the long term management of elephants roaming an
extensive network of habitats may therefore benefit from the opportunity given by the fact
that a reserve like JPNR has only recently opened its boundaries to elephants, hence
providing the chance to monitor this rate of change.
Additionally, the presumption that differences within the compositional diversity between
areas where elephants are absent, compared to areas where they are present, exclusively
result from the browsing of elephants, might be misleading (Kerley et al., 2008). This
suggestion was supported by Landman et al. (2008), who showed that a valid proportion of
available species, in parts where elephants had been excluded, were not consumed by
elephants in the first place. Results of this study explicitly indicated the necessity to consider
multiple variables for the evaluation of cumulative impact in the absence of an unambiguous
agent of cause. In this study, former land-use practices (Chapter 3) and the browsing of
35
Chapter 4
mesoherbivores were considered to potentially represent additional ecological drivers for a
change in the vegetation structure and composition on the study sites. Makhabu et al. (2006)
stressed the importance of a balanced and delicate evaluation of the individual ecological
roles of coexisting mesoherbivores and megaherbivores. They showed that browsing by kudu
(Tragelaphus strepsiceros) and impala (Aepycerus melampus) was facilitated by the creation
of ‘browsing lawns’, which resulted from the modification of large trees by elephants.
Furthermore, plant species that had accumulated elephant impact were favoured to those
without signs of prior utilization by elephants. In conclusion, whether the impact of elephants
appeared to have negative effects on the biological diversity (Cumming et al., 1997; Skarpe
et al., 2004), or whether impact was indicated to be beneficial to flora and fauna (OwenSmith, 1987), management authorities of protective areas that support the coexistence of
megaherbivores and mesoherbivores should turn their back on the ‘single species approach’
and instead describe models that allow for such coexistence of potentially important ‘agents
of system change’, in particular if dealing with a ‘keystone species’ whose ecological role is
interwoven with that of many other species.
36
Chapter 4
4.7 References
Anderson, G.D., Walker, B.H. (1974). Vegetation composition and elephant damage in the
Sengwa Wildlife Research Area, Rhodesia Journal of South African Wildlife
Management Association. 4, pp. 1-14.
Balfour, D., Dublin, H.T., Fennessy, J., Gibson, D., Niskanen, L., Whyte, I.J. (2007). Review
of options for managing the impacts of locally overabundant African Elephants. IUCN,
Gland, Switzerland, pp. 80.
Barnes, R.F.W. (1980). The decline of the baobab tree in Ruaha National Park, Tanzania
African Journal of Ecology. 18, pp. 243-254
Barnes, R.F.W. (1982). Elephant feeding behaviour in Ruaha National Park, Tanzania
African Journal of Ecology. 20, pp.123-136.
Barnes, R.F.W. (1983). The elephant problem in Ruaha National Park, Tanzania Biological
Conservation. 26, pp.127-148.
Barnes, R.F.W. (1985). Woodland changes in Ruaha National Park (Tanzania) between 19761982 African Journal of Ecology. 23, pp. 215-222.
Bax, NP., Bax, L.W., Sheldrick, D.L.W. (1963). Some preliminary observations on the food
of elephants in the Tsavo Royal National Park (East) of Kenya African Journal of
Ecology. 1 (1), pp. 40-51.
Baxter, P.W.J. (2003). Modeling the impact of the African Elephant, Loxodonta Africana, on
woody vegetation in semi-arid savannas. PhD Thesis, University of California,
Berkeley.
Bell, R.H.V. (1971). A grazing ecosystem in the Serengeti Scientific American. 225, pp. 8693.
Bloom, A.J., Chapin, F.S., Mooney, H.A. (1985). Resource limitation in plants – an
economic analogy Annual Review of Ecology and Systematics. 16, pp. 363-392.
Buechner, H.K., Dawkins, H.C. (1961). Vegetation change induced by elephants and fire in
Murchison Falls National Park, Uganda Ecology. 42, pp. 752-766.
Caughley, G. (1976). The elephant problem – an alternative hypothesis East African Wildlife
Journal. 14, pp. 265-283.
Cameron, G.N., Eshelman, B.C. (1996). Growth and reproduction of hispid cotton rats
(Sigmodon Hispidus) in response to naturally occurring levels of dietary protein
Journal of Mammology. 77 (1), pp. 220-231.
37
Chapter 4
Cerling, T.E., Harris, J.M., Leakey, M.G. (1999). Browsing and grazing in elephants: the
isotope record of modern and fossil Proboscideans Oecologia. 120, pp. 364-374.
Cerling, T.E., Passey B.H., Ayliffe, L.K., Cook, C.S., Ehleringer, J.R., Harris, J.M., Dhidha,
M.B., Kasiki, S.M. (2004). Orphan’s tale: seasonal dietary changes in elephants from
Tsavo National Park, Kenya Palaeogeography, Palaeoclimatology, Palaeoecology. 206
(3-4), pp. 367-376.
Cerling, T.E., Wittemeyer, G., Rasmussen, H.B., Vollrath, F., Cerling, C.E., Robinson, T.J.,
Douglas-Hamilton, I. (2006). Stable isotopes in elephant hair document migration
patterns and diet changes Proceedings of the National Academy of Science. 103, pp.
371-373
Chafota, J. (1999). Factors governing selective impacts on woody vegetation. PhD Thesis
(unpublished), Faculty of Science, University of the Witwatersrand, Johannesburg.
Chafota, J. (2007). Factors governing selective impacts on woody vegetation. PhD Thesis,
Faculty of Science, University of the Witwatersrand, Johannesburg.
Chafota, J., Owen-Smith, R.N. (2009). Episodic severe damage to canopy trees by elephants:
interaction with fire, frost and rain Journal of Tropical Ecology. 25, pp. 341-345.
Chaney, W.R. (2003). Why do animals eat the bark and wood of trees and shrubs? Purdue
University, Forestry and Natural Resources, West Lafayette, Indiana.
Clauss, M., Frey, R., Kiefer, B., Lechner-Doll, M., Loehlein, W., Polster, C., Roessner, G.E.,
Streich, W.J. (2003). The maximum attainable body size of herbivorous mammals:
morphophysiological constraints on foregut, and adaptations of hindgut fermenters
Oecologia. 136, pp. 14-27.
Clauss, M., Streich, W.J., Schwarm, A., Ortmann, S., Hummel, J. (2007). The relationship of
food intake and ingesta passage predicts feeding ecology in two different
megaherbivore groups Oikos. 116. Pp. 209-216.
Codron, J., Lee-Thorp, J.A., Sponheimer, M., Codron, D., Grant, R.C., de Ruiter, D.J. (2006).
Elephant (Loxodonta africana) diets in Kruger National Park, South Africa: spatial and
landscape differences Journal of Mammalogy. 87(1), pp. 27-34.
Cromsigt, J.P.G.M., Kuijper, D.P.J. (2011). Revisting the browsing lawn concept:
Evolutionary interactions or pruning herbivore? Perspectives in Plant Ecology and
Systematics. 13 (3), pp. 207-215.
Croze, H. (1974a). The Seronera bull problem. I. The elephants East African Wildlife
Journal. 12, pp. 1-27.
38
Chapter 4
Croze, H. (1974b). The Seronera bull problem. II. The trees East African Wildlife Journal.
12, pp. 29-47.
Cumming, D.H.M., Fenton, M.B., Rautenback, I.L., Taylor, R.D., Cumming, G.S.,
Cumming, M.S., Dunlop, J.M., Ford, G.S., Hovorka, M.D., Johnston, D.S., Kalcounis,
M.S., Mahlanga, Z., Portfors, C.V. (1997). Elephants, woodlands and biodiversity in
miombo woodland in southern Africa South African Journal of Science. 93, pp. 231236.
De Knegt, H.J., Van Langevelde, F., Skidmore, A.K., Delsink, A., Slotow, R., Henley, S.,
Bucini, G., de Boer, W.F., Coughenour, M.B., Grant, C.C., Heitkoenig, I.M.A., Henley,
M., Knox, N.M., Kohi, E.M., Mwakiwa, E., Page, B.R., Peel, M., Pretorius, Y., Van
Wieren, S.K., Prins, H.H.T. (2011). The spatial scaling of habitat selection by African
elephants Journal of Animal Ecology. 80 (1), pp. 270-181.
Demment, M.W., Van Soest, P.J. (1985). A nutritional explanation for body-size patterns of
ruminant and nonruminant herbivores The American Naturalist. 125, pp. 641-672.
Dublin, H.T. (1995). Vegetation Dynamics in the Serengeti-Mara ecosystem: the role of
elephants, fire and other factors, p.71-90. IN: Sinclair, A.R.E., Arcese, P. (eds.)
Serengeti II: Dynamics, Management, and Conservation of an Ecosystem. University of
Chicago Press, Chicago.
Du Toit, J.T. (1990). Feeding-height stratification among African browsing ruminants
African Journal of Ecology. 28, pp. 55-61.
Du Toit, J.T., Bryant, J.P., Frisby, K. (1990). Regrowth and palatability of Acacia shoots
following pruning by African savanna browsers Ecology. 71, pp. 55-61.
Eckhardt, H.C., Van Wilgen, B.W., Biggs, H.C. (2000). Trends in woody vegetation cover in
the Kruger National Park, South Africa, between 1940-1998 African Journal of
Ecology. 38, pp. 108-115.
Field,C.R. (1971). Elephant ecology in the Queen Elizabeth National Park, Uganda East
African Wildlife Journal. 9, pp. 99-123.
Field, C.R., Ross, I.C. (1976). The savanna ecology of Kidepo Valley National Park. II.
Feeding ecology of elephant and giraffe African Journal of Ecology. 14 (1), pp. 1-15.
Gadd, M. (1997). Factors influencing the impact of elephants on woody vegetation in private
protected areas in South Africa’s Lowveld. PhD Thesis, University of Witwatersrand,
Johannesburg.
39
Chapter 4
Gaylard, A., Cadenasso, M.L., Pickett, S.T.A. (2005). Heterogeneity shaped by African
elephants in semi-arid savannas: the significance of space and scale In Review
(BioScience), pp. 36.
Geist, V. (1974). On the relationship of social evolution and ecology in ungulates American
Zoology. 14, pp. 205-220.
Greyling, M.D. (2004). Sex and age related distinctions in the feeding ecology of the African
elephant, Loxodonta africana. PhD Thesis, Faculty of Science, University of the
Witwatersrand, Johannesburg.
Guy, P.R. (1976). The feeding behaviour of elephant (Loxodonta africana) in the Sengwa
area, Rhodesia. South African Journal of Wildlife Research. 6, pp. 55-63.
Holdo, R.M. (2003). Woody plant damage by African elephants in relation to leaf nutrients in
western Zimbabwe Journal of Tropical Ecology. 19, pp. 189-196.
Hulbert, I.A.R., Andersen, R. (2001). Food competition between a large ruminant and small
hindgut fermenter: the case of the roe deer and mountain hare Oecologia. 128, pp. 499508.
Jacobs, O.S. (2001). An autoecological study of the Marula (Sclerocarya birrea) in the
Kruger National Park with specific reference to the relative impact from elephants and
fire. MSc Thesis, University of Pretoria, Pretora, South Africa.
Jarman, P.J. (1974). The social organization of antelope in relation to their ecology
Behaviour. 48, pp. 215-267.
Kalemera, M.C. (1998). Observations on feeding preferences of elephants in the Acacia
tortillis woodland of Lake Manyara, Tanzania African Journal of Ecology. 27 (4), pp.
325-333.
Kerley, G.I.H., Landman, M. (2005). Gardeners of the gods: the role of elephants in the
Eastern Cape Subtropical Thickets In: Grant, C.C. (ed.) A compilation of contributions
by the Scientific Community for SANParks, 2005. Elephant effects on biodiversity: an
assessment of current knowledge and understanding as a basis for elephant
management in SANParks. Scientific Report 3/2005, South African National Parks,
Scientific Services, Skukuza, pp. 173-190.
Kerley, G.I.H., Landman, M., Kruger, L., Owen-Smith, N., Balfour, d., Boer de, W.F.,
Gaylard, A., Lindsay, K., Slotow, R. (2008). Effects of elephants on ecosystems and
biodiversity In: Scholes, R.J., Mennell, K.G. (eds.) Elephant Management - A Scientific
Assessment for South Africa. Wits University Press, Johannesburg, South Africa, pp.
146-205.
40
Chapter 4
Lagendijk, D.D.G., Mackey, R.L., Page, B.R., Slotow, R. (2011). The effects of herivory by
mega- and mesoherbivore on tree recruitment in sand forest, South Africa Plos One.
6 (3), pp. 1-9.
Landman, M., Kerley, G.I.H., Schoeman, D.S. (2008). Relevance of elephant herbivory as a
threat to important plants in the Addo Elephant National Park Journal of Zoology. 274,
pp. 51-58.
Lamprey, H.F., Glover, P.E., Turner, M.I., Bell, R.H.V. (1967). Invasion of the Serengeti
National Park by Elephants East African Wildlife Journal. 5, pp.151-166.
Laws, R.M., Parker, I.S.C., Johstone, R.C.B. (1975). Elephants and their habitats: The
ecology of elephants in North Bunyora, Uganda. Claredon Press, Oxford, United
Kingdom.
Lewis, D.M. (1987). Fruiting patterns, seed germination, and distribution of Sclerocarya
caffra in an elephant-inhabited woodland Biotropica. 19, pp. 50-56.
Lewis, D.M. (1991). Observations of tree growth, woodland structure and elephant damage
on Colophospermum mopane in Luangwa Valley, Zambia African Journal of Ecology.
29, pp. 207-221.
Lidicker, W.Z., Jr. (1975). The role of dispersal in the demography of small mammals, In:
Golley, F.B., Petrusewicz, K., Ryzkowski, L., (eds.) Small mammals: their productivity
and population dynamics. Cambridge University Press, Cambridge, Massachusetts, pp.
103-128.
Makhabu, S.W. (2005). Resource partitioning within a browsing guild in a key habitat, the
Chobe Riverfront, Botswana Journal of Tropical Ecology. 21, pp. 641-649.
Makhabu, S.W., Skarpe, C., Hytteborn, H. (2006). Elephant impact on shoot distribution on
trees and on rebrowsing by smaller browsers Acta Oecologica. 30, pp. 136-146.
Miller, M.F., Coe, M. (1993). Is it advantageous for Acacia seeds to be eaten by ungulates?
Oikos. 66 (2), pp. 364-368.
Mosugelo, D.K.S., Moe, S.R., Ringrose, S., Nellemann, C. (2002). Vegetation changes
during a 36-year period in northern Chobe National Park, Botwana African Journal of
Ecology. 40, pp. 232-240.
O’Connor, T.G., Goodman, P.S., Clegg, B. (2007). A functional hypothesis of the threat of
local extirpation of woody plant species by elephant in Africa Biological Conservation.
136, pp. 329-345.
41
Chapter 4
O’Kane, C.A.J., Duffy, K., Page, B.R., Macdonal, D.W. (2011). Are the long-term effects of
mesobrowsers on woodland dynamics substitutive or additive to those of elephants?
Acta Oecologica. 37, pp. 393-398.
O’Kane, C.A.J., Duffy, K., Page, B.R., Macdonal, D.W. (2012). Heavy impact on seedlings
by the impala suggests a central role in woodland dynamics Journal of Tropical
Ecology. 28, pp. 291-297.
Owen-Smith, R.N. (1983). Dispersal and dynamics of large herbivores in enclosed areas:
implication for management In: Owen-Smith, R.N., (ed.) Management of Large
Mammals in African Conservation Areas. Haum Educational Publishers, Pretoria,
South Africa, pp. 127-144.
Owen-Smith, R.N., Cooper, S.M. (1985). Comparative consumption of vegetation
components by kudu, impalas and goats in relation to their commercial potential as
browsers in savannah regions South African Journal of Science. 81, pp. 72-76.
Owen-Smith, R.N. (1987). Pleistocene extinctions: the pivotal role of Megaherbivores
Paleobiology. 12, pp. 351-362.
Owen-Smith, R.N., Cooper, S.M. (1987a). Assessing food preferences of ungulates by
acceptability indices Journal of Wildlife Management. 51, pp. 373-378.
Owen-Smith, R.N., Cooper, S.M (1987b). Palatability of woody plants to browsing ruminants
in a South African savanna Ecology. 68, pp. 319-331.
Owen-Smith, R.N. (1988). Megaherbivores: the influence of very large body size on ecology.
Cambridge, Cambridge University Press.
Owen-Smith, R.N., Cooper, S.M. (1989). Nutritional ecology of a browsing ruminant, the
kudu (Tralegaphus strepsiceros), through the seasonal cycle Journal of Zoology. 219,
pp. 29-43.
Owen-Smith, R.N., Chafota, J. (2012). Selective feeding by a megaherbivore, the African
elephant (Loxodonta africana) Journal of Mammalogy. 93 (3), pp. 698-705.
Parra, R. (1978). Comparison of foregut and hindgut fermentation in herbivores In:
Montgomery, G.G. (ed.) The ecology of arboreal folivores. Smithsonian Institution
Press, Washington DC, pp. 205-230.
Peel, M. (2012). Ecological monitoring: Association of Private Nature Reserves (Timbavati,
Umbabat, Klaserie and Balule). Tenth Joint Report: 2012, pp. 70. Animal Production
Institute: Livestock Business Division, Nelspruit, South Africa.
Pellew, R.A.P. (1983). The impacts of elephant, giraffe, and fire upon the Acacia tortillis
woodlands of the Serengeti African Journal of Ecology. 21, pp. 41-74.
42
Chapter 4
Petrides, G.A. (1975). Principal foods versus preferred foods and their relations to stocking
rate and range condition Biological Conservation. 7 (3), pp. 161-169.
Prins, H.H.T, Jeugd von der, D. (1993). Herbivore crashes and woodland structure in east
Africa Journal of Ecology. 81, pp. 305-314.
Renecker, L.A., Hudson, R.J. (1986). Seasonal foraging rates in free-ranging moose Journal
of Wildlife Management. 50, pp.143-147.
Ruess, R.W., Halter, F.L. (1990). The impact of large herbivores on the Seronera woodlands,
Serengeti National Park, Tanzania African Journal of Ecology. 28, pp. 259-275.
Shrader, A.M., Bell, C., Bertolli, L., Ward, D. (2012) Forest or trees: At what scale do
elephants make foraging decisions? Acta Oecologica. 42, pp. 3-10.
Skarpe, C., Aarestad, P.A., Andreassen, H.P., Dhillon, S.S., Dimakatso, T., Du Toit, J.T.,
Duncan, J.T., Halley, J., Hytteborn, H., Makhsbu, S., Marl, M., Marokane, W.,
Masunga, G., Modise, D., Moe, S.R., Mojaphoko, R., Mosugelo, G., Sejoe, T.,B.,
Stokke, S., Swenson, J.E., Taolo, C., Vandewalle, M., Wegge, P. (2004). The return of
the giants: ecological effects of an increasing elephant population Ambio. 6, pp. 276282.
Smallie, J.J., O’Connor, T.G. (2000). Elephant utilization of Colophospermum mopane:
possible benefits of hedging African Journal of Ecology. 38, pp. 352-359.
Spencer, C. (2010). Motivation for Olifants West Nature – Reserve status. Internal report.
Olifants West Nature Reserve In: Van der Merwe, F., Spencer, C., (2012) Quantifying
the impact of the African Elephant (Loxodonta africana) on woody plant species within
Olifants West Nature Reserve. Cape Town University of Technology, Republic of
South Africa.
Steves, C.E., Hume, I.D. (1995). Comparative physiology of the vertebrate digestive system.
New York, Cambridge University Press.
Styles, C.V. (1993). Relationships between herbivores and Colophospermum mopane of the
Northern Tuli Game Reserve, Botswana. MSc Thesis, University of Pretoria, Pretoria,
South Africa.
Thomson, P.J. (1975). The role of elephants, fire and other agents in the decline of a
Brachystegia boehmii woodland Journal of the Southern African Wildlife Association.
5, pp. 11-18.
Van Aarde, R.J., Jackson, T.P. (2007). Megaparks for metapopulations: Addressing the cause
of locally high elephant numbers in southern Africa Biological Conservation. 134, pp.
289-297.
43
Chapter 4
Van Koppel, J., Prins, H.T. (1998). The importance of interaction for dynamics of African
woodlands: A hypothesis Journal of Tropical Ecology. 14, pp. 565-576.
Weyerhauser, F.J. (1985). Survey of elephant damage to baobabs in Tanzanias’s Lake
Manyara National Park African Journal of Ecology. 23, pp. 235-243.
Wiseman, R., Page, B.R., O’Connor, T.G. (2004). Woody vegetation change in response to
browsing in Ithala Game Reserve, South Africa South African Journal of Ecology. 26,
pp. 69-71.
44
Chapter 5
Chapter 5
Sex-related distinctions in the dry season feeding ecology of the African
elephant, Loxodonta africana
5.1 Introduction
The African elephant, a large-bodied herbivore, expresses a pronounced sexual dimorphism
with bulls attaining approximately twice the body mass of females (Lee and Moss, 1995).
The biological concept of the allometric relation between digestive anatomy, physiology and
body size was described in detail by Jarman (1974) and Bell (1971) and illustrated in Chapter
4. The consequences of this relation are a reduction in energy requirements with increasing
body mass linked to an isometric scaling of absolute gut size, which in turn results in greater
gastrointestinal capacity and increased mean retention time relative to body size (Demment
and Van Soest, 1985; Owen-Smith, 1988; Stokke and Du Toit, 2000). The ‘Jarman and Bell’
concept (Geist, 1974), referred to as ‘Body Size Hypothesis’ (BSH) by Stokke and Du Toit
(2000), proved not only to provide a profound understanding of large herbivore digestive
physiology and hence distinct dietary requirements in general, but could also be applied to
explain intraspecific and distinct sex-related differences regarding foraging behaviour,
nutritional requirements and hence selectivity, within a species that expresses definite sexual
dimorphism (Perez-Barberia and Gordon, 1998; Ruckstuhl and Neuhaus, 2000). Records
indicated that while non-lactating females and males averaged a mean daily food intake of
approximately 1.0% to 1.2% of the body mass, the mean daily food intake of lactating
females seemed to be about 1.2% to 1.5%, an observation supported by the findings that
energy demands were extremely high in females during pregnancy and lactation (Greyling,
2004; Owen-Smith, 1988). The resulting increase in energy costs and the loss of nutrients
through periods of lactation implies the requirement of a diet comprising higher quality
forage, therefore indicating increasing selectivity. The underlying hypothesis of forage
selection due to sexual dimorphism, as initially proposed by Main and Coblentz, (1996),
suggested potential spatial and temporal segregation, because males and females selected
habitats with distinct plant species availability, differing with regard to quality and nutrient
contents (Ruckstuhl and Neuhaus, 2000). Several studies have focused on these differences in
feeding patterns and diet selection, linking them to sex and size. Outcomes of studies
considering sex-related distinctions in feeding habits, although predominantly consistent
across various habitat types, have recorded deviations from underlying expectations
1
Chapter 5
(Greyling 2004; Shannon et al., 2006a; 2013; Stokke and Du Toit, 2000). Their results
illustrated that females, in general, were more selective than males. Females showed definite
preferences for particular tree species, often exploiting riparian habitats (Smit et al., 2007)
and remaining closer to water sources in general (Stokke and Du Toit, 2002), and for plant
parts with a higher nutritional value (Greyling, 2004; Hiscocks, 1999). Resultant suggestions
of sex-related niche occupation (Shannon et al., 2006a; Stokke and Du Toit, 2000) could
however be misleading in a system of protected areas where artificial water sources are
abundant. Furthermore, Shannon et al. (2006b) reported no significant difference in habitat
choice between male and female elephants and suggested that foraging decisions, which are
potential drivers of sexual segregation, were made at the plant scale rather than at the habitat
level. An additional explanation for sex-related feeding habits was given by Smit et al.
(2007), who considered distinct social obligations, described as ‘collective feeding
requirements’. While bulls may roam independently, females, as part of the family unit,
experienced social constraints (Charif et al., 2005; Dublin, 1996; Mc Comb et al., 2001).
However, as the metabolic rate scales with the factor M0.75 for increasing body size
(Demment, 1983) and mass specific energy requirements decrease while the tolerance for low
quality and high fibre content increases (Demment and Van Soest, 1985), it has to be
considered that sub-adult males, being similar in size to females and hence expressing
comparable energy requirements, could potentially show a feeding behaviour which
resembles that of females rather than of adult bulls (Lee and Moss, 1995). This consideration
implies that overlap might occur, despite distinct sex-related foraging habits.
Bulls were found to spend more time at one feeding site and fed on a broader variety of plant
parts and were therefore considered to be less selective (Shannon et al., 2006a). Furthermore,
they were considerably more destructive and ingested greater quantities of low quality food.
Bulls, which have higher absolute energy demands, were expected to lose out in competition
with females and be displaced into areas with lower quality forage but high in biomass,
(‘Scramble Competition Hypothesis’ Ruckstuhl and Neuhaus, 2000; Shannon et al., 2006a;
Stokke and Du Toit, 2000). It was assumed that if bulls suffered competitive displacement by
females in such a way that they were not able to feed at their preferred feeding height, they
would utilize higher foraging levels that remained inaccessible to the smaller body sized
females (Stokke and Du Toit, 2000). However, the study rejected this hypothesis as males did
not choose to feed at significantly higher levels, although they would have been able to do so.
Shannon et al. (2006a) recorded contrasting results, depending on the study area. While male
and female elephants in Phinda Private Game Reserve, South Africa (PPGR), selected trees
2
Chapter 5
of similar height, bulls in Pongola Game Reserve, South Africa (PGR), were found to select
higher trees than cows in the same reserve. Furthermore, while the preferred feeding height of
elephants in PPGR proved to be 2.2m on average, regardless of their sex, cows in PGR
preferably fed at an average height of 1.8m while bulls browsed at an average height of 2m.
In a different ecosystem, Greyling (2004) found support for the hypothesis of sexual
segregation on the feeding level, as bulls showed significantly higher feeding heights than
family units, preferring to feed at food plots with woody species higher than 5m when
compared to cows. Subsequent research based on movement analyses within the same
ecosystem has additionally shown that bulls preferred areas with higher tree cover (De Knegt
et al., 2011).
Moreover, results of several studies showed that consequences of male feeding behaviour
were more intense than those resulting from female foraging habits. Guy (1976), for example,
recorded that bulls not only felled 80% of the trees in Zimbabwe but that these trees were
profoundly greater in height than those pollarded by females. These findings were supported
by Shannon et al. (2006a), who reported that male elephants on PGR displayed a weightier
and more intense feeding behaviour when compared to female elephants, who rarely
exhibited such behaviour. The foraging intensity of elephants in PPGR, however, did not
seem to depend on their sex and was exhibited similarly in both sexes. It was furthermore
found that bulls on PGR targeted taller trees than cows, and that bulls felled trees more
frequently during the winter months. Additionally, bulls removed a larger amount of biomass
per plant than cows, implying a reduced opportunity for the plant to recover from male
utilization when compared to utilization by female elephants.
While male elephants were observed to consume greater amounts of low quality food and to
feed on a wider selection of plant parts, their preferred range of plant species seemed to be
narrower when compared to females (Stokke and Du Toit, 2000). Accordingly, Shannon et
al. (2006a) reported that cows selected on the basis of a wider and more diverse range of
plant species.
Male elephants were observed to fell trees, dig up roots and break larger branches more
frequently than female elephants, whereas cows were reported to consume leaves, fruit,
smaller branches and the bark more often, comparatively speaking, (Greyling, 2004; Guy,
1976, McNaughton and Georgiadis, 1986; Owen-Smith, 1988; Shannon et al., 2006a)
implying a selection of plant parts consisting of less fibre and a high content of nutrients.
This study does not investigate potential differences in actual browsing (break) heights
between bull groups as opposed to breeding herds. However, it is important to acknowledge
3
Chapter 5
that sexual segregation of feeding patterns may occur at various levels. Stokke and Du Toit
(2000) showed in their study that browsing height was correlated with plant height across all
plant species, which gave rise to the expectation that the analysis of the heights of utilized
plants might nevertheless give an indication of browsing height preferences.
5.1.1 Objectives
The objectives of Chapter 5 were to assess potential distinctions between the sex-related
foraging behaviour of male and female elephants in the semi-arid savanna ecosystem during
the late dry season, thereby describing dietary preferences in a seasonal context, quantifying
woody plant species and plant parts that were utilized, illustrating impact modes that were
displayed and evaluating the intensity of impact.
In consideration of the above literature review, both analysis and discussion were based on
the following assumptions and predictions:
The diet of both sexes consisted of a subset of available woody plant species with the range
of preferred species being somewhat greater but the selection more diverse and determined
for female elephants than for male elephants.
It was beyond the scope of this study to test nutritional values of plants and plant parts.
However, bulls were expected to feed on a wider range of plant parts with higher fibrous
contents (e.g. branches), i.e. on lower quality food, which was predicted to be readily
available during the dry season, while females were expected to be more selective, targeting
high quality sources and preferred plant parts that were known to be highly nutritious in the
late dry season (e.g. bark). Male elephants chose feeding plots with a vegetation cover greater
in height, felled more large trees than females and displayed more intense utilization of the
woody vegetation layer by an increased use of impact modes such as uprooting, main-stem
breakage and breaking off large branches.
Because the sampling period fell within a period of potential resource limitation, i.e. the dry
season, sex-related feeding behaviour was predicted, although not tested, to be more
profound than during the wet summer months when nutritious food is abundant.
4
Chapter 5
5.2 Methods
Refer to Chapter 2 for a detailed description of the study area and the survey methods.
5.3 Data analysis
For a valid interpretation of sex-related differences in foraging behaviour and of the late dry
season diet of male and female elephants, analyses within Chapter 5 were exclusively based
on the ‘new’ backtracking recordings, which allowed for a distinction between the utilization
of woody plants by bull groups as opposed to family units, while additionally considering the
seasonal context. The feeding data of the two social units was furthermore pooled across
study sites as the main objective in this Chapter was to evaluate sex-specific and not sitespecific feeding patterns.
5.3.1 Woody plant species - availability and utilization
In order to determine sex-related preferences for certain plant species, acceptance and
availability indices were evaluated for each species. Site-based acceptability indices (SA)
were calculated by dividing the number of food plots where the species was utilized by the
number of plots in which this species was present. Availability indices (AI) were calculated
by dividing the number of food plots where a species was present by the total number of plots
that were surveyed. Acceptance and availability indices can range from 0 to 1.0 with the
value 1.0 as the maximum, describing, in the case of acceptability, that the species was
utilized in all plots where it was present and, with regard to availability, that the species was
present in all plots that were surveyed.
Identical analyses but on the general feeding ecology, i.e. regardless of sex specific
characteristics, were conducted in Chapter 4 by considering only species that were available
at ten or more food plots in order to avoid the inclusion of rare plants when discussing
acceptability (Owen-Smith and Cooper, 1987a,b). This consideration resulted in a distinct
group of eight core species, comprising Grewia spp., Combretum apiculatum, Acacia
nigrescens, Acacia exuvialis, Acacia erubescens, Commiphora spp., Dichrostachys cinerea
and Sclerocarya birrea. Although the above results were found to apply for bull groups,
Sclerocarya birrea and Acacia exuvialis were only available in four and nine plots
respectively of all the food plots utilized by family units. Sample sizes of recordings for
family units were small in general, only accounting for approximately two thirds of the
recordings for bull groups. However, as the eight species still comprised the majority of
utilized species regardless of sex, they were further used for comparison in analyses in this
5
Chapter 5
Chapter. According to Chapter 4, species were regarded as ‘highly favoured’ if they were not
frequently available (≤0.25) but well accepted (SA ≥0.4). Species that were accepted in a
higher proportion to their availability within the habitat, but not categorized as ‘highly
favoured’, were classified as ‘intermediately preferred’ and species utilized less frequently
than available were characterized as ‘less preferred’, while species with an SA below 0.15
were referred to as ‘neglected’. Species that were consumed in the greatest quantities,
regardless of being preferred or not were described as ‘principle food’.
To assess the relationship between availability indices (Appendix D.1) for woody plant
species in food plots visited by bull groups as opposed to those visited by family units,
thereby determining whether the availability of species differed, a Pearson correlation test (rs)
was applied after the data had been tested for normality. The Pearson correlation test (rs) was
furthermore used to determine how acceptance indices (Appendix D.1) of woody plant
species were correlated between sexes, thereby evaluating whether the two social units fed on
similar woody plant species. The test was repeated with the exclusion of Sclerocarya birrea,
which proved to be the outlier due to its very small sample size with regard to family units.
Finally, the proportional share of each of the eight woody species which contributed to the
late dry season diet was calculated by dividing the number of individual plants that were
impacted within a species by the total number of utilized woody plant individuals, and
compared between social units.
5.3.2 Utilization of trees and shrubs
To test whether observed frequencies matched expected frequencies, a chi-square (χ2) test
was applied, thereby determining potential interaction between the variables ‘herd
demographic’ and ‘utilization of trees and shrubs’, i.e. evaluating whether the utilization of
shrubs and trees depended on whether they were targeted by bull groups or family units.
5.3.3 Acceptance and availability of height classes
Frequencies of available and utilized woody plant species within the ten height classes were
calculated for each social unit and illustrated. Pearson correlation tests (rs) were applied for
each sex after the data had been tested for normality, in order to assess how utilized and
available plants were related within height classes, thereby evaluating whether plants within
distinct height classes were used in relation to availability. A chi-square (χ2) test was applied
to assess a potential interaction between the frequency distribution of height class utilization
and the herd demographic, thereby determining whether the two social units utilized woody
6
Chapter 5
plants of distinct height classes in a similar manner. Additionally, height class acceptance
values (Appendix D.2) were obtained and tested for correlation. Acceptance values were
calculated by dividing the total number of utilized woody plants within a distinct height class
by the total number of woody plants utilized by either family units or bull groups. After the
data had been tested for normality, the Pearson correlation test (rs) was applied to determine
the relationship between the acceptance values for height classes of bulls and those of family
units.
5.3.4 Impact modes, impact mode intensity and plant part utilization
Impact modes
With reference to Chapter 4, impact modes were categorized accordingly: uprooting (ur),
main stem breakage (ms), breaking of larger (primary) branches in order to access smaller
plant parts (bba), branch breaking (bb) and bark-stripping (bs). It must again be noted that
‘impact mode events’ did not equal frequencies of impacted plant individuals, as a single
woody plant could be utilized in more than one way. Sample sizes were generally small and
the use of statistical means thus limited. Descriptive statistics were therefore predominantly
used in order to allow for a sound interpretation of the results. To assess whether the
frequency distribution of impact modes that were displayed by bull groups diverged from
those recorded for family units, the data was tested for normality and a Pearson (rs)
correlation test applied.
Impact mode intensity
The calculation of impact mode intensity was based on impact mode recordings. The
proportional amount of removed biomass served as the decisive factor in determining
whether a utilization event was classified to be of ‘light to moderate’ (0% to 50%) or of
‘heavy’ intensity (50% to 100%). Uprooting and felling events were categorized as ‘heavy
structural change’ as they were observed to affect the vertical vegetation structure more
fundamentally than any other type of utilization. Frequency counts for intensity values were
carried out for both sexes and their proportional share calculated. To determine whether the
two social units affected the woody vegetation with similar intensity, thereby evaluating
whether bulls removed more biomass than females, Spearman’s (ρ) rank correlation was
applied after the data had been tested for normality.
7
Chapter 5
Plant part utilization
For a better understanding of potentially diverging dietary demands between sexes, the
utilization of different plant parts was determined and analysed. According to plant part
classifications in Chapter 4, the snapping of main stems and larger branches in order to access
smaller plant parts was categorized as ‘other’, because the end utilization of distinct plant
parts was often not apparent. Branches stemming from Grewia spp., which were broken off
for consumption of the bark, were counted as ‘grewia bark’ and not presented within the
‘branches’ category. To test whether plant part utilization depended on the variable ‘sex’, a
chi-square (χ2) was applied. Additionally, the proportional share for the different plant parts
which made up the late dry season diet was illustrated for each social unit and tested for
correlation. The frequency distribution of plant part utilization was furthermore demonstrated
across height classes.
5.4 Results
5.4.1 Woody plant species - availability and utilization
Of all the 25 different species that were sampled during the backtracking of feeding paths, a
narrow range of eight species (32% of all available species), of which some were preferred
over others, comprised 95% of the late dry season diet, regardless of sex.
In total, a variety of 24 different species were sampled in feeding plots visited by male
elephants, while 17 different species were recorded in plots utilized by family units. Of the 24
species recorded in plots utilized by bulls, six species in addition to the eight core species,
available in ≤4 plots, contributed 5% of the diet while, of the 17 species encountered by
family units, three species, available in ≤4 plots, supplied the diet with 5%.
A highly significant and very strong positive correlation (Pearson correlation, rs = 0.963,
n = 8, P = <0.001) was found between the availability indices of the eight woody species in
food plots selected by bull groups and those visited by family units, thereby implying that
vegetation composition, with regard to these species, did not significantly differ between
feeding patches selected by bulls and those visited by family units.
Further test results suggested a modest positive correlation, which did not prove to be
statistically significant (Pearson correlation, rs = 0.404, n = 8, P = 0.32), between the
acceptance indices of these woody species when comparing bull groups with family units,
implying that acceptance of distinct species differed between social units. However,
Sclerocarya birrea was found to be the one extreme outlier, most possibly caused by the very
8
Chapter 5
small sample size with regard to family units (available in <4 plots, accepted once). With the
removal of Sclerocarya birrea from the data set, test results returned a highly significant and
strong positive correlation (Pearson correlation, rs = 0.889, n =7, P = 0.007) between
acceptance indices of the two social units, implying that bull groups and family units indeed
accepted particular plant species to a similar extent.
The generally low availability of Sclerocarya birrea, applicable to both social units, was
reflected by Figure 5.1 but was more pronounced for family units. While Sclerocarya birrea
proved to be a ‘highly favouverd’ food source for bull groups (AI = 0.18, SA = 0.92), this
species was ‘intermediately preferred’ by family units (AI = 0.09, SA = 0.3).
Acceptability indices
(a)
1
0.9
0.8
0.7
0.6
0.5
0.4
0.3
0.2
0.1
0
Sbir
Dcin
Aeru
Grew
Capi
Anig
Comi
Aexu
0
0.1
0.2
0.3
0.4
0.5
0.6
0.7
0.8
0.9
1
Availability indices
Acceptability indices
(b)
1
0.9
0.8
0.7
0.6
0.5
0.4
0.3
0.2
0.1
0
Grew
Aeru
Sbir
Aexu
0
Figure 5.1
0.1
Dcin
Anig
0.2
0.3
Capi
Comi
0.4 0.5 0.6 0.7
Availability indices
0.8
0.9
1
Acceptance and availability indices for woody species that were available at ten or
more food plots used by bull groups (a) and family units (b).
Preferred spp. = red; intermediately preferred spp. = yellow; less preferred spp. = light green;
Sbir=Sclerocarya birrea, Aeru=Acacia erubescens, Anig=Acacia nigrescens,
Dcin=Dichrostachy cinerea, Comi=Commiphora spp., Aexu=Acacia exuvialis,
Capi=Combretum apiculatum, Grew=Grewia spp.
9
Chapter 5
Acacia erubescens was ‘highly favouverd’ by both bull groups (AI = 0.19, SA = 0.43) and
family units (AI = 0.22, SA = 0.40). Acacia nigrescens (AI = 0.26, SA = 0.37), Acacia
exuvialis (AI = 0.15, SA = 0.18) and Dichrostachys cinerea (AI = 0.30, SA = 0.50) could be
described as ‘intermediately preferred spp.’ for bull groups, with Acacia nigrescens
seemingly being on the verge of becoming a ‘preferred’ food source. Acacia nigrescens (AI
= 0.24, SA = 0.27) was ‘intermediately preferred’ by family units. Grewia spp. (AI = 0.91,
SA = 0.74), Combretum apiculatum (AI = 0.50, SA = 0.26), Commiphora spp. (AI = 0.40,
SA = 0.30), Acacia exuvialis (AI = 0.20, SA = 0.10) and Dichrostachys cinerea (AI = 0.39,
SA = 0.38) were proportionally less abundant within the diet of family units than they were
available within the habitat, with the acceptance of Dichrostachys cinerea being in very close
relation to the species availability within the habitat. Species that were proportionally less
abundant within the diet of bull groups than they were available within the habitat were
Commiphora spp. (AI = 0.39, SA = 0.31), Combretum apiculatum (AI = 0.58, SA = 0.33) and
Grewia spp. (AI = 0.88, SA = 0.54). Grewia spp. represented the ‘principal food source’ for
both social units but were found to be more frequently available in food plots selected by
family units and more readily accepted by family units as opposed to bull groups. Grewia
spp. constituted half (49%) of the entire diet of bull groups and comprised 62% of the diet
consumed by family units (Figure 5.2).
10
Chapter 5
(a)
Dichrostachys
cinerea
(17) 11%
Commiphora spp.
(9) 6%
Acacia erubescens
(7) 5%
Sclerocarya birrea
(14) 9%
Combretum
apiculatum
(14) 9%
Acacia nigrescens
(7) 5%
Acacia exuvialis
(3) 2%
other: 6 spp.
available in <4 plots
5% (7)
Grewia spp.
(76) 49%
(b)
Combretum
apiculatum
(8) 8%
Dichrostachys
cinerea
(11) 11%
Commiphora spp.
(2) 2%
Acacia erubescens
(5) 5%
Sclerocara birrea
(1) 1%
Acacia nigrescens
(3) 3%
Acacia exuvialis
(3) 3%
Grewia spp.
(63) 62%
other: 3 spp.
available in <4 plots
5% (5)
Figure 5.2
Relative dietary contributions of the eight woody plant species that were commonly
utilized by bull groups (a) and family units (b) during the late dry season, study sites
combined. The value in brackets shows the number of utilized individuals within
each species.
11
Chapter 5
Dichrostachys cinerea (11%) contributed the second largest share to the diet of both sexes.
The third largest proportional share of the late dry season diet was represented by Combretum
apiculatum, applying to both sexes (bulls: 9%; families: 8%) and Sclerocarya birrea (9%)
with regard to bull groups, while Sclerocarya birrea (1%) was only utilized once in all
feeding plots visited by family units (CAVE: low availability). Acacia erubescens accounted
for 5% of the total diet, regardless of sex. The relative dietary contribution of Acacia
nigrescens proved to be higher with respect to bull groups (5%) as opposed to family units
(3%), whereas the relative utilization of Acacia exuvialis was higher for family units (3%)
compared to bull groups (2%). While Commiphora spp. only comprised 2% of the female
elephants’ diet, they supplied 6% of the diet selected by bull groups.
5.4.2 Utilization of trees and shrubs
No significant interaction was found between the variables ‘herd demographic’ and
utilization of shrubs (chi-square test, χ2 = 0.026, df = 1, P = 0.871) and trees (chi-square test,
χ2 = 0.765, df = 1, P = 0.382), implying that bull groups and family units utilized shrubs and
trees in similar proportions with regard to availability (Figure 5.3). Both social units utilized
approximately one third (bulls: 31%; families: 32%) of all available shrubs and less than one
third (bulls: 23%; families: 26%) of all available trees.
100%
90%
80%
70%
60%
50%
40%
30%
20%
10%
0%
(b)
68%
69%
Percentage of utiized trees
Percentage of utilized shrubs
(a)
not
utilized
shrubs
utilized
shrubs
32%
31%
bull groups
family units
74%
77%
26%
23%
family units
Demographics
Proportional utilization and negligence of shrubs (a) and trees (b)
- bull groups versus family units.
12
not
utilized
trees
utilized
trees
bull groups
Demographics
Figure 5.3
100%
90%
80%
70%
60%
50%
40%
30%
20%
10%
0%
Chapter 5
5.4.3 Acceptance and availability of height classes
The utilization of specific height classes did not depend on whether bull groups or family
units were feeding, as returned test results were not significant (chi-square test, χ2 = 11.623,
df = 7, P = 0.114). The correlation between height class acceptance values (Figure 5.4) of
bull groups and family units, furthermore proved to be significant and strongly positive
(Pearson correlation, rs = 0.827, n = 10, P = 0.003), implying that the relative utilization of
height classes was similar between social units.
Acceptance values - family units
0.30
0.25
0.20
hc 4
hc 7
0.15
hc 10
hc 6
0.10
hc 9
0.05
hc 1
hc 2
0.00
0.00
hc 5
hc 3
hc 8
0.05
0.10
0.15
0.20
0.25
0.30
Acceptance values - bull groups
Figure 5.4
Relationship between acceptance values for height classes 1 to 10 of bull groups and
family units.
With regard to the frequency values of utilized and available plants (Table 5.1) within distinct
height classes, it can be said that the majority of woody plants that were utilized by bull
groups were present in height classes with the most available individuals, implying that
utilization took place in relation to availability. This observation only partly applied to family
units, which predominantly utilized plants in heights of 1.5m through 3.5m and above 4.5m.
The most frequently used height classes were hc 4, hc 6 and hc 7, of which hc 4 (1.5m-2m)
and hc 7(3m-3.5m) were the most abundant, i.e. plants from these two classes were used in
relation to availability. The above implication of utilization in relation to availability was,
however, supported by a very strong and statistically significant correlation between available
and accepted woody plants within height classes for bull groups (Pearson correlation, rs =
0.939, n = 10, P = <0.001) and family units (Pearson correlation, rs = 0.882, n = 10,
P = 0.001), furthermore indicating that exploitation within height classes did not occur
inversely to height class availability.
13
Chapter 5
Table 5.1
Frequency distribution of available and utilized woody plants across height classes
- bull groups versus family units.
Green markings: the three most available height classes.
Pink markings: the three most utilized height classes.
height
class
1
0-0.5m
2
0.5-1m
3
1-1.5m
4
1.5-2.0m
5
2.0-2.5m
6
2.5-3.0m
7
3.0-3.5m
8
3.5-4.0m
9
4.0-4.5m
10
>4.5m
Pearson correlation (rs)
Total
bull groups
100%
utilized
availability
plants
2
0
20
0
74
14
137
39
95
25
56
12
40
18
46
13
24
7
76
26
family units
utilized
100%
plants
availability
0
1
0
12
6
46
22
69
13
48
16
39
20
52
5
21
10
25
14
50
P = <0.001
570
P = 0.001
154
107
363
To summarize, both sexes utilized plants from height class 3 (1m) through height class 10
(>4.5m), with the most prolific utilization occurring for plants between 1.5m and 2.5m and
above 4.5m with regard to bull groups, while family units preferably fed on woody plants
between 1.5m and 2m and between 2.5m and 3.5m in height. Plants within height class 4
(1.5m to 2.0m) were the preferred target for both social units.
14
Chapter 5
5.4.4 Impact modes, impact mode intensity and plant part utilization
Impact modes
Impact modes recorded for bull groups and those observed to be displayed by family units
were strongly and positively correlated (Pearson correlation, rs = 0.999, n = 5, P = <0.001),
implying that both sexes exhibited impact modes in similar proportions (Table 5.2).
Table 5.2
Distribution of impact mode frequencies and their proportional share
- social units compared
ur=uprooting, ms=mainstem breakage, bba=breaking of larger branches in order to
access smaller plant parts, bb=branch breaking, bs=bark-stripping
ur
ms
bb
bba
bs
total
no impact
bull groups
34
21%
11
7%
106
64%
7
4%
7
4%
165
100%
416
family units
27%
30
5%
6
62%
70
4%
5
2%
2
100%
113
256
Pearson correlation: rs = 0.999, n = 5, P = <0.001
‘Branch breaking’ proved to be the most prolific impact mode, regardless of sex (bulls: 64%,
families: 62%), followed by ‘uprooting’. Family units (27%) uprooted proportionally more
woody plants than bull groups (21%), while bulls felled (ms) slightly more woody plants (7%
versus 5%), compared to females. The impact modes ‘bark-stripping’ (bs) and ‘breaking of
larger branches’ (bba) each made up 4% of all the impact events by bulls. ‘Bark-stripping’
events by family units were recorded twice and bba events were documented five times,
accounting for 2% and 4% respectively in relation to other impact modes.
15
Chapter 5
Impact mode intensity
The frequency distribution of impact mode intensity values for the two social units were
found to be positively correlated (Spearman’s rank correlation, ρ = 0.807), implying that male
and female elephants had a similar impact on the woody vegetation layer with regard to
intensity.
Table 5.3
Impact mode intensity - Frequency of occurrence and proportional share of ‘heavy’
(orange) and ‘light to moderate’ (green) impact - social units compared.
ur=uprooting, ms=main stem breakage, bba=breaking of larger branches in order to
access smaller plant parts, bb=branch breaking, bs=bark-stripping
bull groups
frequ.
%
45
27%
1
1%
6
4%
40
24%
66
40%
1
1%
6
4%
165
100%
impact mode intensity
(ur+ms) heavy structural change
(bba) heavy intensity
(bba) light to moderate intensity
(bb) heavy intensity
(bb)light to moderate intensity
(bs) heavy intensity
(bs) light to moderate intensity
total impact
family units
%
frequ.
32%
36
3%
3
2%
2
12%
14
50%
56
<1%
1
1%
1
100%
113
Of all utilization events displayed by family units and by bull groups, 32% and 27%
respectively resulted in ‘heavy structural change’ that originated from the uprooting and
felling of woody plants. However, it has to be noted that 70% of all the plants that were
uprooted by family units and 65% of plants that were uprooted by bulls, were members of the
Grewia spp., while 70% of the 30% trees which were uprooted by family units were smaller
plants below 4m, with the same applying for 50% of the 35% trees which were uprooted by
bull groups. Of all the trees felled by family units, 50% were recorded to be below 4m in
height, while of all the trees snapped (ms) by bulls 55% comprised trees below 4m in height.
The breaking of larger branches (bba) rarely resulted in heavy intensity for either bull groups
(1%) or family units (3%), while bba events that involved a removal of biomass below 50%
were recorded six times (4%) for bull groups and twice (2%) for family units. Events of
branch breaking (bb) that entailed a removal of biomass below 50% were the most prolific
incidents recorded for both sexes (bulls: 40%, families: 60%). Branch breaking events that
involved a removal of biomass above 50% had a proportional share of 12% (14) for family
units, while that impact mode was recorded 40 times (24%) with intense results for bull
groups. Bark-stripping (bs) of trees, regardless of intensity, had a proportional share of ≤1%
16
Chapter 5
in relation to other impact mode events documented for females, while this mode of
utilization was recorded once for bull groups (1%) as resulting in a loss of biomass above
50%, and six times (4%) as being of ‘light to moderate intensity’. In summary, 53% of all
utilization events displayed by bull groups resulted in a loss of biomass of more than 50% or
in ‘structural change’, while the same applied for 47% of impact mode events that were
recorded for family units.
Plant part utilization
The proportional utilization of different plant parts (Figure 5.5) by elephants within the study
area was independent (chi-square test, χ2 = 2.822, df = 4, P = 0.588) of whether feeding was
recorded for bull groups or family units. This result was supported by a strong and positive
correlation (Pearson correlation, rs = 0.958, n = 5, P = 0.01) between the frequency
distribution values of utilized plant parts by bull groups and family units.
(a)
11%
(18)
(b)
10%
(11)
21%
(34)
roots
roots
branches
branches
bark trees
33%
(54)
27%
(30)
bark trees
35%
(39)
bark grewia
bark grewia
other (ms&bba)
other (ms&bba)
4% (7)
Figure 5.5
32%
(52)
2% (2)
27%
(31)
Proportional share of plant part utilization within the late dry season diet of bull
groups (a) and family units (b), with the frequency values in brackets.
branches=small branches that were consumed partly or entirely
bark grewia=broken branches of grewia spp. that were exclusively used for their bark
bark trees=utilization of bark following bark-stripping mode events of larger trees
bark total=bark grewia combined with bark trees
other=unidentified utilization of plant parts following ms or bba events
About 90% of the diet comprised the pooled consumption of roots, bark from Grewia spp.
and branches, with each category contributing a share of approximately one third. While the
relative consumption of branches was slightly higher for bull groups (32% versus 27%), roots
contributed slightly more to the diet of family units (27% versus 21%). The proportional
share of ‘Grewia bark’ was almost identical between sexes (bulls: 33%, families: 35%), while
the bark of trees contributed 4% (bulls) and 2% (families) to the late dry season diet.
17
Chapter 5
Plant parts that supplied the late dry season diet of bull groups and family units originated
from woody plants above 1m in height, with the majority coming from plants between 1.5m
and 3.5m and above 4.5m, regardless of sex (Figure 5.6).
(a)
40
40
35
35
30
30
other
(ms&bba)
bark grewia
25
20
15
Frequencies
Frequencies
(b)
bark trees
other
(ms&bba)
bark grewia
25
20
15
bark trees
10
10
branches
5
5
branches
0
0
Height classes 1-10, in metres
Figure 5.6
Height classes 1-10, in metres
Frequency distribution of utilized plant parts across height classes by bull groups (a)
and family units (b).
Grewia shrubs that were utilized for their bark mostly fell within a range of heights between
1m and 3.5m for both sexes, while the bark originating from trees was stripped from
individuals above 3m in height, with bulls predominantly using trees above 4.5m in height.
Roots and branches that were consumed by bull groups and family units stemmed from
woody plants across all height classes without any apparent pattern, except for the more
pronounced use of large trees by bull groups. Bulls displayed a more pronounced utilization
of plant parts from large trees above 4.5m than family units, implying exclusive access to
quality forage at heights that could not be reached by the smaller females.
18
Chapter 5
5.5 Discussion
Shannon et al. (2006b) examined potential drivers of sexual segregation in the African
elephant as a dimorphic species and found that, although both sexes displayed distinct
selective behaviour with regard to habitat choice (Shrader et al., 2012) as opposed to
randomly roaming the available area, sexual segregation at habitat scale was not apparent. In
contrast, Smit et al. (2007) described that although an overlap of spatial distribution between
bulls and families existed, sexual segregation at habitat scale did occur, with mature bulls
roaming the area extensively and utilizing areas where mixed herds were generally absent, a
behaviour that was assumed to be impossible for mixed herds due to social constraints and
specific nutritional requirements.
Shannon et al. (2006b) concluded that sexual segregation in elephants might not
predominantly be influenced by sex-related habitat choice but rather at scales such as
sociality and distinct sex-related foraging behaviour at the plant species level. Stokke and Du
Toit (2002) suggested furthermore that it was more likely to be the proximity of water rather
than vegetation quality that proved to drive sexual segregation at the habitat scale, because
reproductively mature females and their offspring were more constrained in terms of spatial
distribution than socially independent roaming males. This valid point was, however, decided
not to influence habitat choice in this study, as artificial water sources were abundant within
the study area. Although resource scarcity during the winter months was predicted to
potentially increase distinct sex-related feeding behaviour, such profound differences in
selection and preferences at the plant species level could not be confirmed by this study.
Results supported findings by Greyling (2004), who reported the lack of marked differences
in the diet selection at the plant species level for elephants in the APNR. The main share of
the late dry season diet analysed within this study comprised a narrow subset of the same
eight species for both social units, indicating similar selective preferences with regard to diet
range. Acceptability of species seemed to be in concordance between sexes and distinctions
between preferences of certain species within the core range of the diet appeared to be
marginal, which might however have been caused by the comparatively small sample size for
family units. Results agreed with findings of Greyling (2004) and Shannon et al. (2006a),
who reported that female and male elephants in the APNR and in PGR fed on a selection that
was comparatively narrow in view of the total number of species available. Greyling (2004)
reported that 70% to 80% of the diet consisted of a limited range of six to eight plant species,
while Shannon et al. (2006a) recorded a selected range of six species, which comprised 70%
19
Chapter 5
of the entire diet. However, the latter study also documented distinct sex-related feeding
behaviour by showing that females generally displayed a preference for a larger variety for
tree species, whereas they spent less time at one feeding site than males. This observation
agreed with results by Stokke (1999) and Stokke and Du Toit (2000), who found that male
elephants in Chobe National Park spent a considerably longer time at one feeding site than
females. They furthermore found that females, compared to males, selected feeding paths
which not only provided more plant individuals in total but which had higher species
diversity, implying a higher degree of selective foraging opportunities on potentially high
quality food. Females selected forage patches which provided the highest density of palatable
species when compared with the surrounding area, while males did not discriminate between
patches in terms of species diversity.
Greyling (2004) argued that elephant densities could be one explanation for the lack of
distinct feeding patterns at the plant species level, a consideration which was likely to apply
to observations of this study. Personal observation confirmed low densities of elephants on
OWNR and on JPNR during the study period. The relatively low density of elephants
reported for Greylings’ (2004) study in the APNR (0.41 elephants/km2) and the density
recorded for BNR by Peel (2012) (0.67 elephants/km2) might have resulted in a reduction of
intraspecific competition, resource partitioning and selection pressure. In contrast, high
densities of elephants, reported to range from 7 elephants/km2 to 12 elephants/km2 (Gibson,
1998) in Chobe National Park, might inevitably result in sex-related dietary differences
because of increased competition during a period of potential resource scarcity during the dry
season. In the search for high quality food in order to meet underlying dietary requirements,
female elephants would therefore be expected to exhibit increased selectivity on a wider
range of species under such conditions, an implication that was confirmed by the results of
Stokke and Du Toit (2000), as illustrated above.
The lack of sex-specific preferences at the plant species level, as observed in this study, could
furthermore potentially be explained by the veld condition. The last four years have provided
good rainfall (see Appendix D.3/4 for rainfall data, provided by G. Reinstorf and
G.Thomson), which could be expected to result in a thriving bush that even in the late dry
season would provide abundant low quality food and sufficient high quality forage for
competition and sexual segregation at the plant part and species level to become redundant.
Furthermore, it is suggested that the true species richness of the study area (Chapter 3) might
be skewed towards relatively few common and abundantly available species and many rare
20
Chapter 5
species, which might explain that the bulk of the diet, regardless of sex, comprised a limited
range of the same eight commonly available species.
Contrary to Greyling (2004), who reported an inverse relation between acceptance and
availability of height classes, the results of this study confirmed that the selection of height
classes occurred in relation to availability. In general, woody plants between 1.5m and 3.5m
and above 4.5m were well accepted, with woody plants between 1.5m and 2m in height being
preferred over others, regardless of sex. These results supported studies which found the
actual browsing height to be below 2m above ground for male and female elephants
(Greyling, 2004; Guy, 1976; Shannon et al. 2006a, Stokke and Du Toit, 2000). In agreement
with Stokke and Du Toit (2000) and earlier findings (Jachmann and Bell, 1985; Jachmann
and Croes, 1991), the majority of utilized plants were below 4m and above 1m, which could
indicate, as suggested in Chapter 4, that due to the palatability of coppice growth, elephants
preferred re-browsing on plant individuals on which they had previously fed (Anderson and
Walker, 1974; Cromsigt and Kuijper, 2011; Jachmann and Bell, 1985; Lewis, 1991). Neither
sex showed interest in woody plants below 1m. Contrary to the prediction that bulls chose
feeding plots with a vegetation cover greater in height, the relative availability of shrubs and
trees above 3.5m was equal (26%) in food plots selected by bulls and those chosen by family
units. This observation could possibly be explained by the pronounced importance of shade
for calves and juveniles. Bull groups utilized 34% of all available trees above 4.5m, while
family units included 28% of such tall trees in their feeding bouts. Bull groups, furthermore,
utilized more plant parts originating from larger trees than family units. This could imply that
male elephants, due to their larger body size, made use of the exclusive access to resources in
heights inaccessible for the smaller females (Owen-Smith, 1988). Accordingly, Greyling
(2004) found that bulls more frequently targeted tall trees for breaking of large branches.
Shannon et al. (2006a) reported divergent results with the social units on one reserve feeding
at a similar average height, while male elephants on the other reserve targeted significantly
taller trees than their sexual counterpart.
If the ‘Scramble Competition Hypothesis’ applied for elephants in the study area, bulls would
have been expected to be displaced from preferred browsing heights and potentially high
quality food sources selected by female elephants. This study did not confirm these
expectations, which implied that competition between male and female elephants was low or
absent with regard to plant height. Again, it has to be pointed out that relatively low elephant
densities and excellent veld conditions at the time of sampling might have resulted in browse
availability above levels at which competitive displacement among the sexes would have
21
Chapter 5
been likely to set in. Because the majority of utilized plants fell within a range of heights well
accessible to smaller mesoherbivores, it is furthermore suggested that such favourable browse
conditions would not only result in decreased intraspecific competition but also in a decline
of interspecific competition. If interspecific competition was significant, implying resource
limitation at the preferred feeding height, both male and female elephants could potentially
have switched to browse that was inaccessible for most mesoherbivores. These considerations
were supported by Stokke and Du Toit (2000), who showed that adult females fed at higher
levels when feeding in close proximity to sub-adults and offspring compared to when feeding
alone, therefore confirming that intraspecific competition, and hence assumedly interspecific
displacement, at the patch feeding level indeed occurred when energy demands would
otherwise not be met.
In addition to the lack of significant sex-related feeding patterns on the plant species level,
similar results were obtained for analyses on levels of plant part and mode of utilization. Bull
groups and family units which were recorded during the study period not only displayed a
proportionally equal frequency of divergent impact modes but also failed to show a
significant difference in the use of plant parts, implying a diet of similar nutritional value.
In contrast, Greyling (2004) and Stokke and Du Toit (2000) showed that male elephants
snapped large branches significantly more often and more frequently uprooted and felled
trees, while family units were more often observed to defoliate branches and to bark-strip.
Shannon et al. (2006a) reported accordingly that plant parts which were consumed during
feeding bouts differed significantly between sexes. Female elephants more frequently chose
leaves, smaller branches, fruit and flowers compared to bulls. The findings of these studies
implied that female elephants targeted plant parts with lower contents of fibre and potentially
higher nutritious values, therefore indicating the urge to meet higher energy demands than
those of bull elephants and hence supporting the ‘Body Size Hypothesis’.
The diet of elephants within this study, regardless of sex, consisted of almost similar shares
that amounted to 90% of roots, bark and smaller branches, indicating a diet corresponding to
the dry season, when nutrients are stored and cycled in roots and bark (Bloom et al., 1985;
Owen-Smith and Cooper, 1989), and stems represent a critical resource reserve once
abscission has taken place (Bell, 1986). These results were found to be in agreement with
records by Owen-Smith and Chafota (2012), who documented a dry season diet that
comprised 94% bark, roots and twigs. Accordingly, Barnes (1982) reported that the dry
season diet of elephants in the Ruaha National Park included woody tissue of more than 50%
in the cool dry season, ranging to more than 80% towards the end of the dry season. The
22
Chapter 5
proportional share of branch browsing was found to be slightly higher for bull elephants
(32% versus 27%), while family units proportionally consumed marginally more roots (27%
versus 21%), results that could imply a diet higher in fibre and lower in nutrients for bulls.
These implications however, need further investigation by means of ongoing monitoring
efforts, which would allow for larger sample sizes.
The expectation, that male elephants affect the woody vegetation more intensively by
removing larger amounts of biomass during their feeding bouts, was based on the theory that
larger body sized bulls consumed higher quantities of fibrous food than female elephants
(Demment, 1983). Family units are furthermore subject to social constraints in as much that
movement depends on the group and the duration of feeding bouts is therefore shorter,
implying potentially less impact on the plant individual (Fritz and de Garine Wichaititsky,
1996; Shannon et al., 2006a). Greyling (2004) and Shannon et al. (2006a), for example,
reported that feeding bouts of bull groups resulted in a markedly larger loss of biomass than
those of family units.
Although utilization events of high intensity in this study were
recorded more often for bull groups (53%) than for family units (47%) and the proportional
share of branch breaking events that resulted in a plant’s biomass removal of above 50%, was
twice as high for bull groups in comparison with the value recorded for family units (24%
versus 12%), results were statistically insignificant.
In contrast to the findings of this study, which described a relatively higher utilization of
roots by families than by bulls, several studies reported that bulls uprooted proportionally
more trees than family units, whereby these uprooting events furthermore targeted larger
trees than uprooting activities observed for family units (Greyling, 2004; Shannon et al.,
2006a; Stokke and Du Toit, 2000). The pronounced sex-related difference in body size and
hence pure strength might present a straightforward explanation for the preferred targeting of
large trees by bulls, thereby allowing access to plant parts in the upper canopy (Croze, 1974a;
Jachmann and Bell, 1985), while potentially serving social ranking purposes (Guy, 1976;
Lamprey et al., 1967). The vast majority of plants targeted for uprooting purposes by both
social units in this study, however, consisted of Grewia shrub species and smaller trees below
4m, which could possibly explain the relatively high share of uprooting events by family
units. Energy demands, potentially satisfied by nutrients stored within the root system, would
thereby be met to a certain extent without the obligation to topple large trees.
Personal observation agreed with recordings by Greyling (2004), who noted that family units
regularly broke off branches of Grewia spp. and consequently debarked these stems, thereby
gaining access to the nutritional phloem tissue, while continuing walking to allow for group
23
Chapter 5
cohesion (Fritz and de Garine Wichaititsky, 1996; Shannon et al., 2006a). Bulls in this study
debarked proportionally more large trees than family units (4% versus 2%) but were also
observed to frequently use stems of Grewia spp. in order to access the high quality tissue.
The similarity in display of such feeding behaviour might plainly be explained by the easy
access and abundant availability of Grewia spp.
The lack of a distinct sex-related feeding behaviour on levels of impact mode, impact mode
intensity and plant part utilization might again find its explanation in the lush veld conditions
and low elephant densities. Such conditions could possibly suppress the establishment of a
situation where family units outcompete bulls (Stokke and Du Toit, 2000), thereby obliging
them to switch to a diet which is high in fibre and low in nutrients, hence leading to marked
differences in feeding patterns on the various levels discussed above.
A further explanation which appeared to be valid for a better understanding of the available
results could be found in the current population demography. The vast majority of bull
groups observed on both study sites and whose feeding paths were sampled, were bachelor
herds comprising young adult bulls in their late teens and early to mid and late 20s. Only 19%
of all backtracking efforts recorded feeding bouts by mature bulls that were estimated to be
around 30 years old (pers. obsverv.). A young adult male at the age of 20 years is roughly
half the weight of a fully grown mature bull (Laws et al., 1975). The ‘Body Size Hypothesis’
predicts that, due to body size differences, the mass-specific energy demands of younger and
smaller sized individuals, which have additional energetic costs owing to growth, are greater
than those of fully grown adults of the same species (Ruckstuhl, 2003; Smit et al., 2007). Bon
et al. (2001) and Ruckstuhl (1998) suggested that different feeding patterns and segregation
might therefore not only occur between the two sexes of a dimorphic species but also
between age groups of the same sex. The feeding behaviour of young adult male elephants, as
recorded within this study, might hence more likely resemble the foraging behaviour of
family units than that of adult males, reflecting energy requirements similar to those of adult
females.
24
Chapter 5
5.6 Summarizing conclusion and implications
The first prediction was confirmed, in that both sexes only fed on a subset of all available
species, but rejected in that the diet range between sexes did not differ and family units did
not, as expected, feed on a greater and more diverse selection of plant species.
Moreover, it could neither be confirmed that bull elephants fed on a wider range of plant
parts than family units, while selecting a high proportion of woody components thereby
implying a diet of high fibre and low quality, nor was it documented that family units,
compared to bull groups, selected significantly more high quality resources potentially low in
fibre and high in nutrients.
Sex-related selection of feeding plots with regard to the proportional height class distribution
of available and targeted plants was furthermore not apparent. The proportional share of
utilization modes displayed by family units and bull groups did not significantly differ,
thereby rejecting the expectation that bulls exhibited a more marked and intense utilization of
the woody vegetation layer in activities such as uprooting and tree felling more often than
family units.
The underlying biological concept of the ‘Body Size Hypothesis’, its consequences with
regard to sex-related foraging behaviour in a sexual dimorphic species and the potentially
arising sexual segregation on a social and habitat level, as well as at plant species and plant
part scale, could prove essential for a profound understanding of the elephant’s spatial and
temporal distribution, its resource use and the resulting impact on habitat composition and
structure. Outcomes of this study implied however, that for a valid interpretation of local
feeding patterns and their ecological consequences, this concept needs to be enhanced beyond
the level of potential sex-related differences in foraging decisions and additionally be applied
to foraging behaviour of different age groups of the same species and potentially the same
sex, as discussed above and confirmed by Ruckstuhl (1998). An evaluation of the influence
on ecosystem function caused by such size-related behaviour, in combination with knowledge
of the demographic distribution and movement analyses of the local elephant population,
might therefore have fundamental implications for the conservation and management of the
ecosystem and the African elephant as a dimorphic species (Bowyer, 2004).
25
Chapter 5
References
Anderson, G.D., Walker, B.H. (1974). Vegetation composition and elephant damage in the
Sengwa Wildlife Research Area, Rhodesia Journal of South African Wildlife
Management Association. 4, pp. 1-14.
Barnes, R.F.W. (1982). Elephant feeding behaviour in Ruaha National Park, Tanzania
African Journal of Ecology. 20, pp. 123-136.
Bell, R.H.V. (1971). A grazing ecosystem in the Serengeti Scientific American. 225, pp. 8693.
Bell, R.H.V. (1985). Elephants and woodlands – a reply Pachyderm. 5, pp.17-18.
Bloom, A.J., Chapin, F.S., Mooney, H.A. (1985). Resource limitation in plants – an
economic analogy Annual Review of Ecology and Systematics. 16, pp. 363-392.
Bon, R., Rideau, C., Villaret, J., Joachim, J. (2001). Segregation is not only a matter of sex in
Alpine ibex Capra ibex ibex Animal Behaviour. 62, pp. 495-504.
Bowyer, R.T. (2004). Sexual segregation in ruminants: definitions, hypotheses and
implications for conservation and management Journal of Mammalogy. 85, pp. 10391052.
Charif, R.A., Ramey, R.R., Langbauer, W.R., Payne, K.B., Martin, R.B., Brown, L.M.,
(2005). Spatial relationships and matrilineal kinship in African savanna elephant
(Loxodonta africana) clans Behavioural Ecologyand Sociobiology. 57, pp. 327-338.
Croze, H. (1974a). The Seronera bull problem. I. The elephants East African Wildlife
Journal. 12, pp. 1-27.
De Knegt, H.J., Van Langevelde, F., Skidmore, A.K., Delsink, A., Slotow, R., Henley, S.,
Bucini, G., de Boer, W.F., Coughenour, M.B., Grant, C.C., Heitkoenig, I.M.A., Henley,
M., Knox, N.M., Kohi, E.M., Mwakiwa, E., Page, B.R., Peel, M., Pretorius, Y., Van
Wieren, S.K., Prins, H.H.T. (2011). The spatial scaling of habitat selection by African
elephants Journal of Animal Ecology. 80 (1), pp. 270-181.
Demment, M.W. (1983). Feeding ecology and the evolution of body size of baboons African
Journal of Ecology. 21, pp. 219-233.
Demment, M.W., Van Soest, P.J. (1985). A nutritional explanation for body-size patterns of
ruminant and nonruminant herbivores The American Naturalist. 125, pp. 641-672.
Dublin, H.T. (1996). Elephants on the Masai Mara, Kenya: seasonal habitat election and
group size patterns Pachyderm. 22, pp. -35.
26
Chapter 5
Fritz, H., de Garine Wichaititsky, M. (1996). Foraging in a social antelope: effects of group
size on foraging choices and resource perception in impala Journal of Animal Ecology.
65, pp. 736-742.
Geist, V. (1974). On the relationship of social evolution and ecology in ungulates American
Zoology. 14, pp. 205-220.
Gibson, D.St.C., Craig, G.C., Masogo, R.M. (1998). Trends of the elephant population in
northern Botswana from aerial survey data Pachyderm. 25, pp. 14-27.
Greyling, M.D. (2004). Sex and age related distinctions in the feeding ecology of the African
elephant, Loxodonta africana. PhD Thesis, Faculty of Science, University of the
Witwatersrand, Johannesburg.
Guy, P.R. (1976). The feeding behaviour of elephant (Loxodonta Africana) in the Sengwa
area, Rhodesia. South African Journal of Wildlife Research. 6, pp. 55-63.
Hiscocks, K. (1999). The impact of increasing elephant population on the woody vegetation
in southern Sabi Sand Wildtuin, South Africa Koedoe. 42, pp. 47–55.
Jachmann, H., Bell, R.H.V. (1985). Utilization by elephants of the Brachystegia woodlands in
the Kasungu National Park, Malawi African Journal of Ecology. 23, pp. 235-253.
Jachmann,
H.,
Croes,
T.
(1991).
Effects
of
browsing
by
elephants
on
the
Combretum/Terminalia woodland at the Nazinga Game Ranch, Burkina Faso, West
Africa Biological Conservation. 57, pp. 13-24.
Jarman, P.J. (1974). The social organization of antelope in relation to their ecology
Behaviour. 48, pp. 215-267.
Lamprey, H.F., Glover, P.E., Turner, M.I., Bell, R.H.V. (1967). Invasion of the Serengti
National Park by elephants East African Wildlife Journal. 5, pp. 151-166.
Laws, R.M., Parker, I.S.C., Johstone, R.C.B. (1975). Elephants and their habitats: The
ecology of elephants in North Bunyora, Uganda. Claredon Press, Oxford, United
Kingdom.
Lee, P.C., Moss, C.J. (1995). Statural growth in known-age the African elephant (Loxodonta
africana) Journal of Zoology, London. 236, pp. 29-41.
Lewis, D.M. (1991). Observations of tree growth, woodland structure and elephant damage
on Colophospermum mopane in Luangwa Valley, Zambia African Journal of Ecology.
29, pp. 207-221.
Main, M.B., Coblentz, B.E. (1996). Sexual segregation in ungulates: a reply Journal of
Mammalogy. 79, pp. 1410-1415.
27
Chapter 5
McComb, K., Moss, C., Durant, S.M., Baker, L., Syialel, S. (2001). Social knowledge in
African Elephants Science. 292, pp. 491-494.
McNaughton, S.J., Georgiadis, N.J. (1986). Ecology of African grazing and browsing
mammals Annual Review of Ecology, Evolution and Systematic. 17, pp. 39-56.
Owen-Smith, R.N. (1988). Megaherbivores: the influence of very large body size on ecology.
Cambridge, Cambridge University Press.
Owen-Smith, R.N., Cooper, S.M. (1989). Nutritional ecology of a browsing ruminant, the
kudu (Tralegaphus strepsiceros), through the seasonal cycle Journal of Zoology. 219,
pp. 29-43.
Owen-Smith, R.N., Chafota, J. (2012). Selective feeding by a megaherbivore, the African
elephant (Loxodonta africana) Journal of Mammalogy. 93 (3), pp. 698-705.
Peel, M. (2012). Ecological monitoring: Association of Private Nature Reserves (Timbavati,
Umbabat, Klaserie and Balule). Tenth Joint Report: 2012, pp. 70. Animal Production
Institute: Livestock Business Division, Nelspruit, South Africa.
Perez-Barberia, F.J., Gordon, J. (1998). The influence of sexual dimorphism in body size and
mouth morphology on diet selection and sexual segregation in cervids Acta Veterinaria
Hungarica. 46, pp. 357-367.
Ruckstuhl, K.E. (1998). Foraging behaviour and sexual segregation in bighorn sheep Animal
Behaviour. 55, pp. 99-106.
Ruckstuhl, K.E., Neuhaus, P. (2000). Sexual segregation in ungulates: a new approach
Behaviour. 137, pp. 361-377.
Rucksthl, K.E. (2003). Bite rates in rocky mountain bighorn sheep (Ovis Canadensis): effects
of season, age, sex and reproductive status Behavioural Ecology and Sociobiology. 54,
pp. 167-173.
Shannon, G., Page, B.R., Duffy, K.J., Slotow, R. (2006a). The role of foraging behaviour in
the sexual segregation of the African elephant Oecologica. 150, pp. 344-354.
Shannon, G., Page, B.R., Duffy, K.J., Slotow, R. (2006b). The consequences of body size
dimorphism: are African elephants sexually segregated at the habitat scale? Behaviour.
143, pp. 1145-1168.
Shrader, A.M., Bell, C., Bertolli, L., Ward, D. (2012) Forest or trees: At what scale do
elephants make foraging decisions? Acta Oecologica. 42, pp. 3-10.
Smit, I.P.J., Grant, C.C., Whyte, I.J. (2007). Landscape-scale sexual segregation in dry
season distribution and resource utilization of elephants in Kruger National Park, South
Africa Diversity and Distributions. 13, pp. 225-236.
28
Chapter 5
Stokke, S. (1999). Sex differences in feeding-patch choice in a megaherbivore: elephants in
Chobe National Park, Botswana Canadian Journal of Zoology. 77, pp. 1723-1732.
Stokke, S., Du Toit, J. (2000). Sex and size related differences in the dry season feeding
patterns of elephants in Chobe National Park, Botswana Ecography. 23, pp. 70-80.
Stokke, S., Du Toit, J. (2002). Sexual segregation in habitat use by elephants in Chobe
National Park, Botswana African Journal of Ecology. 40, pp. 360-371.
29
Chapter 6
Chapter 6
6.1 Conclusion, Limitations, Recommendations
Nature Reserves that join the greater network of Protected Areas, therefore becoming an
‘open system’ that will function as a ‘dispersal sink’, potentially experience an initial increase
in elephant numbers until metapopulation dynamics set in and natural fluctuations between
source and sink subpopulations take place (Van Aarde and Jackson, 2007). These areas will
have to expect an impact on the woody vegetation structure, which might be of increased
intensity at first, while potentially resulting in an alteration of the vegetation community in
the long run. Such impact can give rise to severe aesthetic concern, but at the same time
further investigation is necessary to determine to what extent the promotion of serious and
alarming ecological concern is justified and how faunal and floral biodiversity are influenced.
It has furthermore yet to be confirmed whether elephant-induced alterations of the vegetation
decreases species diversity or rather result in a shift of species (Owen-Smith, 1988), with the
reduction of habitat for some and the creation of niches for others. Many studies, including
this one, have confirmed that the interaction between elephants and their ecosystem is
complex and not easily, if at all, predictable. Elephant-habitat interaction and its driving
forces such as habitat choice, distribution patterns, population dynamics, density and, last but
not least, the elephant’s feeding ecology are fundamental research areas that, only in
combined consideration, will nourish our understanding and should assist in the attempt to
develop the most appropriate management models.
This study was time limited and confined to a small area. It therefore represents a temporal
and spatial snapshot. While structural change within the woody vegetation layer was
confirmed, only continuing long-term surveys can identify whether species composition and
diversity will be altered or reduced as survival and regeneration over time would be recorded.
Nevertheless, results and discussions have once more accentuated the intricacy of the
‘elephant problem’ by identifying several factors, other than elephants, which potentially
affect the structure and community of the woody vegetation layer and which can therefore not
be ignored when management decisions are made. Factors such as general herbivory, rainfall,
historical land use practices, past management regimes and the lack of a ‘natural yardstick’
must inevitabely be considered when establishing management strategies.
The description of models that allow for a manageable coexistence of mega- and
mesoherbivores appeared to be of particular importance to this study. While high herbivore
numbers shape the vegetation structure in general, a combination of potential seedling and
1
Chapter 6
sapling predation by smaller herbivores like impala and the impact on larger reproductively
mature trees by elephants could seriously slow down woodland regeneration in particular.
Limitations of this study predominantly resulted from the existing time constraints and
recommendations for future research approaches were therefore outlined.
While only the late dry season feeding behaviour and its impact on the woody vegetation was
sampled in this study, future monitoring programmes should establish long-term protocols
that allow for a crucial interpretation of seasonality within the elephant’s feeding behaviour
and its consequences on the alteration of the woody vegetation over time. It furthermore
needs to be noted that the relatively high amount of rainfall during preceding wet seasons
potentially resulted in dry season veld conditions that were lacking the expected resource
limitations, therefore likely moderating the impact that elephants would exert on the woody
vegetation during times of severe forage scarcity. The sampling protocol should, moreover,
be altered in such a way that permanent transects are visited on a monthly basis, gradually
decreasing the frequency to a three-month interval in order to catch the consequences over
time. Field observations and the backtracking of fresh feeding paths should, on the contrary,
be conducted more often, therefore detailing our comprehension of sex- and size-related
feeding patterns in a seasonal context, while improving our understanding of habitat use in a
spatial context. Preferred plant species and less preferred species should furthermore be
analysed for their nutritional values for a better understanding of selective feeding patterns. In
order to allow for a valid interpretation of impact in habitats characterised by potentially
varying vegetation communities, it is moreover suggested to either set up additional transects
or to randomly generate individual sampling plots across the landscape of the two reserves.
For adaptive management decisions it is suggested that the coupling of sampling regimes
with updated information on elephant movement patterns and demographics, within the area
and beyond, would be of advantage firstly, in order to track the potential effects of dispersal
across the heterogeneous savanna system and secondly, because habitat utilization, its local
concentration and the arising pattern of habitat and vegetation alteration are expected to
mirror elephant distribution (Baxter, 2003; Steyn and Stahlmans, 2001; Van Wyk and Fairall,
1969). The heterogeneous nature of the savanna ecosystem indicates a rather patchy
distribution of nutrient hotspots and hence suggests habitat utilization by the elephant in a
mosaic manner, moreover implying that neighbouring environments which appear to be
similar might suffer a very dissimilar impact (Duffy et al., 2002). This consideration
emphasizes the idea that ‘absolute elephant densities’ might be a rather arguable tool to
2
Chapter 6
measure and predict impact outcomes (Steyn and Stahlmans, 2001) and stresses the need for
analyses on finer scales.
Although out of scope and therefore not evaluated during this study, variables that have been
suggested to drive elephant distribution patterns, such as fences and artificial water sources
(Loarie et al., 2009; Smit et al., 2007; Stokke and Du Toit , 2002; Vanak et al., 2010),
furthermore need to be taken into account. Loarie et al. (2009) emphasized that natural
seasonality of elephant distribution might become absent in an environment that is somehow
or partly confined and where water is abundant. They reported that elephants intensively
utilized resources in the proximity of fences during the dry season, consequently increasing
pressure on the woody vegetation in such areas. They furthermore suggested that artificial
water sources would result in dry season home ranges that comprised areas which, under
natural conditions, would only be visited and hence impacted during the wet season, when
home ranges of the elephant, a water dependent species (Smit et al., 2007b), become more
relaxed. Consequences of such provoked movement patterns are the overexploitation of
sensitive areas that hence miss out on the ‘natural’ period of regeneration which would
seasonally set in without such artificial park design. Personal observation has confirmed that
elephants on JPNR were occupying certain areas between waterholes for days, moving back
and forth between waterholes and therefore potentially over-utilizing this habitat. The
exceptional attention given to the two variables, fences and surface water, appeared to be
justifiable when considering management options in ‘corner’ reserves like OWNR and JPNR,
in which the spatial design reflects a dead end that does not allow the animals to simply
traverse but forces them to bounce off the fence, and where artificial water sources are
abundant.
In view of these implications it is therefore crucial to allow for site-specific monitoring
programmes, while simultaneously assuring the integration of management decisions with
adjacent Reserves and preferably beyond, because resource acquisition and its consequences
for distribution patterns will possibly involve a larger area (Thomas et al., 2011). The
consideration of a wider spatial scale appears therefore to be essential for the productive and
adaptive management of a highly mobile species like the African elephant (Kerley et al.,
2008). The understanding of food and water availability as the crucial driving forces for
spatial habitat utilization by elephants (Van Aarde et al., 2006) underlines the relevance of
ongoing monitoring programmes that involve both variables. The need for a detailed
comprehension of such factors that drive the utilization of space within regional areas is
gaining in importance as elephant populations are expanding and ‘dispersal’ proposed to be a
3
Chapter 6
valuable management tool (Owen-Smith et al., 2006; Thomas et al., 2011; Van Aarde et al.,
2006; Van Aarde and Jackson).
The implementation of ‘non-intervention’ management regimes is questionable with regard to
the elephant as a ‘long-lived’ species that reproduces at a low rate with generally low
mortality (Lindsay, 1996; Owen-Smith, 1988; Woolley et al., 2008). Gough and Kerley
(2006), for example, investigated density dependent regulation mechanisms in the population
within Addo Elephant National Park, where stocking rates were up to 4 elephants/km2, but
could not confirm such mechanisms. They suggested, therefore, that density dependent
regulation mechanisms ought not to be promoted for the controlling of elephant numbers in
areas where resources are abundant and not seasonally limited. Density independence in
population growth was furthermore recorded for the majority of elephant populations that
were studied by Van Jaarsveld et al. (1999) in South Africa, while Van Aarde et al. (1999)
reported that regulation of population growth by density dependence mechanisms in the KNP
would only set in at densities greater than 0.37 elephants/km2. Other studies which developed
equilibrium models, in contrast, inferred trends for density dependence in population growth
(Chamaille-Jammes et al., 2007; Sinclair, 2003). However, in an area where dispersal is
optional and the elephant not regulated by resource limitation because water and potentially
forage are abundant, the application of non-intervention management regimes needs to be
considered carefully, as it remains questionable whether natural density dependence
mechanisms would set in.
It has often been concluded that elephant impact will result in a change in state from
woodland to grassland, therefore negatively influencing biodiversity (Buechner and Dawkins,
1961; Cumming et al., 1997; Jachmann and Croes, 1991; Laws, 1970; Ruess and Halter,
1990). Gaylard et al. (2005) suggested that these conclusions of elephant impact were based
on a flawed interpretation of a ‘balance of nature’ paradigm, which predicts a homogenous
spread of impact across the landscape. In a heterogeneous semi-arid savanna system,
however, such changes in woodland cover would, although still occurring, not spread
homogeneously but at multiple spatial scales in a patchy manner. These considerations,
furthermore, supported the idea that the management of park-wide and absolute elephant
densities might not be the right approach, inferring the preference to work at multiple scales.
In areas where the distribution of surface water is extensive and ubiquitous, the natural
conditions of heterogeneity might, however, be reduced and replaced by homogeneity that
would, in turn, entail changes of state at broad scales (Gaylard et al., 2005), furthermore
complicating the evaluation of system conditions and hence the decision making.
4
Chapter 6
The ‘balance of nature’ concept and the view of the savanna ecosystem as a stable state,
which will always return to its equilibrium state, was furthermore regarded as obsolete by
Gillson and Lindsay (CITES, 2002), who recognized that the alteration of habitat over long
time scales was a normal part of semi-arid savanna ecosystems, where moderate disturbance
appeared essential to maintain biodiversity. They argued that patch-wise and temporal
alternation between woodland and grassland is a natural pattern of savanna vegetation, which
does not represent a ‘climax’ vegetation state but rather reflects a system of ‘multiple stable
states’ (Dublin et al., 1990) that might be the ecological norm in a habitat historically and
naturally dominated by elephants.
It lies beyond the scope of this study to provide definite solutions. However, results should be
used as a baseline stepping stone, which can be built upon by the establishment of succeeding
and ongoing monitoring programmes. Additionally, it is suggested to acquire a profound
understanding of habitat choice and habitat use of the African elephant, while further
investigating the practicability of non-intrusive management strategies that would involve
dispersal and resource acquisition, with a particular focus on the potential manipulation of
artificial water sources.
In conclusion, it is essential to acknowledge that emotionally charged discussions on the
management of elephants in general, and their impact on the woody vegetation in particular,
frequently lack the consideration of the savanna ecosystem as a fluctuating system, that is
based on a complex interaction of various ecosystem components, rather than as a state of a
stable equilibrium. The emotional reactions and aesthetic concern, which are based on
personal observations of landowners and stakeholders, should not be regarded as trivial or
foolish. However, ecological concern can only be justified by scientific evidence, evoking the
strong need for ongoing research. At the end of the day it is the management’s responsibility
to overcome this delicate gap between ecological and aesthetic concerns and to make
ecologically appropriate decisions.
Van Aarde (2005) made a point:
“The conservation of elephants makes sense. As an umbrella species their requirements for
space and resources include those of most other species. As a keystone species their presence
(or absence) modifies the structure and function of ecosystems. As a flagship species we tend
to identify with their needs and conservation.”
5
Chapter 6
6.2 References
Baxter, P.W.J. (2003). Modeling the impact of the African Elephant, Loxodonta Africana, on
woody vegetation in semi-arid savannas. PhD Thesis, University of California,
Berkeley.
Buechner, H.K., Dawkins, H.C. (1961). Vegetation change induced by elephants and fire in
Murchison Falls National Park, Uganda Ecology. 42, pp. 752-766.
Chamaille-Jammes, S., Valeix, M., Fritz, H. (2007). Managing heterogeneity in elephant
distribution: interactions between elephant population density and surface-water
availability Journal of Applied Ecology. 44, pp. 625-633.
CITES Briefing Document (2002). Ecological reality to cull (eds.) Gillson, L., Lindsay, K.
Cumming, D.H.M., Fenton, M.B., Rautenbach, I.L., Taylor, R.D., Cumming, G.S.,
Cumming, M.S., Dunlop, J.M., Ford, G.S., Hovorka, M.D., Johnston, D.S., Kalcounis,
M.C., Mahlanga, Z., Portfors,, C.V. (1997). Elephants, Woodlands and Biodiversity in
South Africa South African Journal of Science. 93, pp. 231-236.
Duffy, K.J., Os van, R., Vos, S, Aarde van, J., Ellish, G., Stretch, A-M.B. (2002). Estimating
impact of reintroduced elephant on trees in a small reserve South African Journal of
Wildlife. 32, pp. 23-29.
Dublin, H.T., Sinclair, A.R.E., McGlade, J. (1990). Elephants and fire as causes of multiple
stable states in the Serengeti-Mara woodlands Journal of Animal Ecology. 59, pp. 11471164.
Du Toit, J.T. (1990). Feeding height stratification among African browsing ruminants African
Journal of Ecology. 28, pp. 55-61.
Gaylard, A., Cadenasso, M.L., Pickett, S.T.A. (2005). Heterogeneity shaped by African
elephants in semi-arid savannas: the significance of space and scale In Review
(BioScience), pp. 36.
Gough, K.F., Kerley, G.I.H. (2006). Demography and population dynamics in the Addo
Elephant National Park, South Africa: is there evidence of density dependent
regulation? Oryx. 40 (4), pp. 434-441.
Jachmann,
H.,
Croes,
T.
(1991).
Effects
of
browsing
by
elephants
on
the
Combretum/Terminalia woodland at the Nazinga Game Ranch, Burkina Faso, West
Africa Biological Conservation. 57, pp. 13-24.
6
Chapter 6
Kerley, G.I.H., Landman, M., Kruger, L., Owen-Smith, N., Balfour, d., Boer de, W.F.,
Gaylard, A., Lindsay, K., Slotow, R. (2008). Effects of elephants on ecosystems and
biodiversity In: Scholes, R.J., Mennell, K.G. (eds.) Elephant Management - A Scientific
Assessment for South Africa. Wits University Press, Johannesburg, South Africa, pp.
146-205.
Laws, R.M. (1970). Elephants as agents of habitat and landscape change in East Africa
Oikos. 21, pp. 1-15.
Lindsay, K. (1996). Studying elephant – habitat interactions In: Kangwana, K. (ed.) Studying
Elephants. African Wildlife Foundation, Kenya, pp. 90-97.
Loarie, S.R., van Aarde, R.J., Pimm, S.T. (2009). Fences and artificial water affect African
savannah elephant movement patterns Biological Conservation. 142, pp. 386-398.
Owen-Smith, R.N. (1988). Megaherbivores: the influence of very large body size on ecology.
Cambridge, Cambridge University Press.
Owen-Smith, R.N., Kerley, G.I.H., Page, B., Slotow, R., Van Aarde, R.J. (2006). A scientific
perspective on the management of elephants in the Kruger National Park and elsewhere
South African Journal of Science. 102, pp. 389-394.
Ruess, R.W., Halter, F.L. (1990). The impact of large herbivores on the Seronera woodlands,
Serengeti National Park, Tanzania African Journal of Ecology. 28, pp. 259-275.
Sinclair, A.R.E. (2003). Mammal population regulation, keystone processes and ecosystem
dynamics Philosophical Transactions of the Royal Society London B. 358, pp. 17291740.
Smit, I.P.J., Grant, C.C., Whyte, I.J. (2007a). Elephants and water provision: what are the
management links? Diversity and Distributions. 13, pp. 666-669.
Smit, I.P.J., Grant, C.C., Devereux, B.J. (2007b). Do artificial waterholes influence the way
herbivores use the landscape? Herbivore distribution patterns around rivers and
artificial surface water sources in a large African savannah park Biological
Conservation. 136, pp. 85-99.
Steyn, A., Stahlmans, M. (2001). Selective habitat utilization and impact on vegetation by
African elephant within a heterogenous landscape Koedoe. 44, pp. 95-103.
Stokke, S., duToit, J. (2002). Sexual segregation in habitat use by elephants in Chobe
National Park, Botswana African Journal of Ecology. 40, pp. 360-371.
Thomas, B., Holland, J.D., Minot, E.O. (2011). Seasonal home ranges of elephants
(Loxodonta africana) and their movements between Sabi Sand Reserve and Kruger
National Park African Journal of Ecology. 50, pp. 131-139.
7
Chapter 6
Van Aarde, R.J, Whyte, I., Pimm, S. (1999). Culling and the dynamics of the Kruger National
Park African elephant population Animal Conservation. 2, pp. 287-294.
Van Aarde, R.J., Jackson, T.P., Ferreira, S.M. (2006). Conservation science and elephant
management in Southern Africa South African Journal of Science. 102, pp. 385-388.
Van Aarde, R.J. (2005). The conservation management of elephants in southern Africa: reestablishing metapopulation dynamics across megaparks In: SAWMA Newsletter,
January 2005 – South African Wildlife Management Association, pp. 43-44.
Van Aarde, R.J., Jackson, T.P. (2007). Megaparks for metapopulations: Addressing the cause
of locally high elephant numbers in southern Africa Biological Conservation. 134, pp.
289-297.
Vanak, A.T., Thaker, M., Slotow, R. (2010). Do fences create an edge-effect on the
movement patterns of a highly mobile mega-herbivore? Biological Conservation. 143,
pp. 2631-2636.
Van Jaarsveld, A.S., Nicholls, A.O., Knight, M.H. (1999). Modelling and assessment of
South African elephant Loxodonta africana Environmental Modelling and Assessment.
4, pp. 155-163.
Van Wyk, P., Fairall, N. (1969). The influence of the African elephant on the vegetation of
the Kruger National Park Koedoe. 12, pp. 57-89.
Young, K.D., Ferreira, S.M., Van Aarde, R.I. (2009).The influence of increasing population
size and vegetation productivity on elephant distribution in the Kruger National Park
Austral Ecology. 34, pp. 329-342.
Woolley, L-A., Mackey, R.L., Page, B.R., Slotow, R. (2008). Modelling the effect of agespecific mortality on elephant Loxodonta africana populations: can natural mortality
provide regulation? Oryx. 42 (1), pp. 49-57.
8
Appendix A
Appendix A – Chapter 2
Figure A 1.1 OWNR study site - elephant route and transect locations
Figure A 1.2 JPNR study site - elephant route and transect locations
1
Appendix B
Appendix B – Chapter 3
Table B.1
Woody plant species sampled during the late dry season on both study sites;
if only recorded on one site, this is indicated by the name of the site.
Scientific Name
Acacia robusta
Acacia erubescens
Acacia exuvialis
Acacia karoo
Acacia nigrescens
Acacia tortillis
Albizia harveyi
Boscia albitrunca
Capparis tomentosa
Combretum apiculatum
Combretum hereorense
combretum imberbe
Commiphora spp.
Dalbergia melanoxon
Dichrostachys cinerea
Ehritia amoena
Euclea divinorum
Euclea undulata
Grewia spp.
Lannea schweinfurthii
Lonchocarpus capassa
Ormocarpum trichocarpum
Pappea capensis
Peltophorum africanum
Sclerocarya birrea
Sterculia rogersii
Terminalia prunoides
Ximenia americana
Ziziphus mucronata
Schotia brachypetala
Common Name
Ankle thorn
Blue thorn
Sweet thorn
Flaky thorn
Knob thorn
Umbrella thorn
Common false-thorn
Shepherd's tree
Woolly caper-bush
Red bushwillow
Russet bushwillow
Leadwood
Corkwood
Zebrawood
Sickle bush
Sandpaper bush
Magic guarri
Common guarri
Raisin bush spp.
False Marula
Apple-leaf
Caterpillar bush
Jacket plum
Weeping wattle
Marula
Common star-chestnut
Lowveld cluster-leaf
Blue Sourplum
Buffalo-thorn
Weeping boer-bean
2
OWNR
JPNR
JPNR
OWNR
JPNR
JPNR
OWNR
OWNR
Appendix B
Table B.2
Age estimation for the marula tree (Sclerocarya birrea) populations on
OWNR and JPNR- basal circumference measurements in cm and age in years
(from regression equation adopted from Haig (1999)).
OWNR
OWNR
JPNR
JPNR
y=0.7643*x+0,0405 y=0.7643*x+0,0405 y=0.7643*x+0,0405 y=0.7643*x+0,0405
basal circ.
age basal circ.
age
basal circ.
age
basal circ.
age
cm
yrs
cm
yrs
cm
yrs
cm
yrs
21.0
16.1
119.0
91.0
52.0
39.8
156.0
119.3
43.0
32.9
120.0
91.8
78.0
59.7
157.0
120.0
45.0
34.4
124.0
94.8
85.0
65.0
157.0
120.0
51.0
39.0
124.0
94.8
85.0
65.0
158.0
120.8
56.0
42.8
125.0
95.6
90.0
68.8
159.0
121.6
63.0
48.2
126.0
96.3
94.0
71.9
160.0
120.8
70.0
53.5
129.0
98.6
96.0
73.4
163.0
124.6
71.0
54.3
132.0
100.9
97.0
74.2
164.0
125.4
71.0
54.3
132.0
100.9
100.0
76.5
166.0
126.9
74.0
56.6
133.0
101.7
106.0
76.5
167.0
124.6
79.0
60.4
134.0
102.5
108.0
82.6
197.0
150.6
82.0
62.7
135.0
103.2
111.0
84.9
207.0
158.3
85.0
65.0
136.0
104.0
111.0
84.9
216.0
165.1
88.0
67.3
137.0
104.8
113.0
86.4
221.0
169.0
92.0
70.4
140.0
107.0
113.0
86.4
average
103.3
92.0
70.4
140.0
107.0
123.0
94.0
93.0
71.1
148.0
113.2
126.0
96.3
95.0
72.6
156.0
119.3
133.0
101.7
95.0
72.7
157.0
120.0
135.0
103.2
98.0
74.9
160.0
122.3
137.0
104.7
100.0
76.5
160.0
122.3
139.0
106.3
107.0
81.8
162.0
123.9
139.0
106.3
108.0
82.6
169.0
129.2
141.0
107.8
109.0
83.4
184.0
140.7
149.0
106.3
109.0
83.4
187.0
143.0
150.0
114.7
110.0
84.1
average
85.1
151.0
115.4
110.0
84.1
152.0
116.2
3
Appendix C
Appendix C – Chapter 4
Table C.1
Availability (paired t-test, P=0.87; Pearson correlation, P = 0.005) and
Acceptance (paired t-test, P=0.27; Pearson correlation, P = 0.004) indices for
the eight core species - study sites compared.
Species
Grewia
Capi
Anig
Dcin
Comi
Aeru
Aexu
Sbir
Paired t-test
Pearson correlation
Table C.2
OWNR
JPNR
AI
AI
0.85
0.89
0.63
0.58
0.31
0.24
0.24
0.38
0.55
0.32
0.21
0.16
0.05
0.26
0.16
0.11
P = 0.87
P = 0.005
OWNR
JPNR
SA
SA
0.79
0.63
0.54
0.33
0.37
0.32
0.53
0.49
0.35
0.30
0.46
0.53
0.00
0.21
0.90
0.60
P = 0.27
P = 0.004
Height class acceptance ratios (utilized shrubs or trees per height class in
relation to height class availability) for ‘new’ (a) (paired t-test, P=0.093;
P=0.312) and ‘old’ (b) (paired t-test, P=0.255; P=0.540) impact respectively study sites compared.
(a)
height
class
1
2
3
4
5
6
7
8
9
10
shrub
shrub
OWNR
JPNR
0.00
0.00
0.00
0.00
0.09
0.10
0.25
0.21
0.15
0.33
0.21
0.28
0.29
0.40
0.33
0.29
0.29
0.33
0.20
0.50
P=0.093
tree
tree
OWNR
JPNR
0.00
0.50
0.00
0.09
0.11
0.00
0.08
0.06
0.10
0.06
0.40
0.17
0.17
0.31
0.10
0.36
0.32
0.29
0.21
0.33
P=0.312
4
Appendix C
(b)
height
class
1
2
3
4
5
6
7
8
9
10
Table C.3
shrub
shrub
OWNR
JPNR
0.00
0.60
0.33
0.41
0.61
0.41
0.54
0.59
0.72
0.66
0.71
0.68
0.79
0.65
0.75
0.71
0.43
0.83
0.40
0.75
P=0.255
tree
tree
OWNR
JPNR
0.00
0.00
0.12
0.18
0.11
0.40
0.30
0.38
0.38
0.25
0.48
0.41
0.42
0.49
0.42
0.52
0.53
0.43
0.58
0.52
P=0.540
Acceptance ratios (utilized shrubs or trees per height class in relation to
height class availability) for ‘new’ (Wilcoxon signed-rank test, P=0.889) and
‘old’ (paired t-test, P=<0.001) impact data - pooled across study sites.
height
class
1
2
3
4
5
6
7
8
9
10
new'
new'
pooled
pooled
shrub
tree
0.00
0.25
0.00
0.04
0.10
0.07
0.23
0.07
0.25
0.08
0.26
0.28
0.36
0.25
0.31
0.21
0.31
0.30
0.33
0.27
P=0.889
old'
old'
pooled
pooled
shrub
tree
0.38
0.00
0.37
0.14
0.50
0.21
0.57
0.33
0.68
0.31
0.69
0.44
0.70
0.46
0.72
0.46
0.62
0.47
0.56
0.54
P=0.00
5
Appendix C
Table C.4
Frequency distribution of impact modes across height classes for ‘new’
impact (a) and ‘old’ impact (b) - study sites compared.
ur=uprooting, ms=mainstem breakage, bba=breaking of larger branches in order to
access smaller plant parts, bb=branch breaking, bs=bark-stripping
(a)
height
class
1
2
3
4
5
6
7
8
9
10
SUM
ur
OWNR JPNR
0
0
0
0
3
2
3
9
1
5
1
4
4
5
2
5
4
2
4
6
22
38
ms
OWNR JPNR
0
0
0
0
0
1
0
1
0
1
0
1
1
4
0
0
2
2
3
5
6
15
bba
OWNR JPNR
0
0
0
0
0
0
0
0
0
0
0
1
0
0
0
0
1
0
7
5
8
6
bb
OWNR JPNR
0
0
0
0
5
7
20
17
12
19
9
15
7
22
4
2
3
7
12
15
72
104
bs
OWNR JPNR
0
0
0
0
0
0
0
0
0
0
0
0
0
0
1
1
1
0
3
12
5
13
no impact
OWNR JPNR
1
3
17
14
42
58
57
98
37
69
24
43
18
36
25
23
15
16
24
56
260
416
bb
OWNR
0
9
40
63
57
21
23
20
10
27
270
bs
OWNR JPNR
0
0
0
0
0
0
0
2
1
0
0
0
1
1
0
1
0
2
13
12
15
18
no impact
OWNR JPNR
5
4
23
18
40
47
70
63
37
55
21
33
20
29
22
17
16
16
34
45
288
327
(b)
height
class
1
2
3
4
5
6
7
8
9
10
SUM
ur
OWNR
0
0
0
1
0
1
0
0
1
16
19
JPNR
0
0
2
4
3
2
4
2
0
9
26
ms
OWNR JPNR
0
0
0
2
0
2
3
1
1
1
3
2
1
0
2
2
0
2
4
6
14
18
bba
OWNR JPNR
0
0
0
0
1
0
1
2
1
0
2
0
0
1
3
0
0
0
23
21
31
24
6
JPNR
2
6
28
75
67
46
35
16
13
36
324
Appendix C
Table C.5
Sum of impact mode frequencies for ‘new’ (a) impact data (chi-square test,
χ2 = 4.252, df = 4, P = 0.373) and for ‘old’ (b) impact data (chi-square test,
χ2 = 2.777, df = 4, P = 0.596) - study sites compared.
ur=uprooting, ms=mainstem breakage, bba=breaking of larger branches in order to
access smaller plant parts, bb=branch breaking, bs=bark-stripping
(a)
(b)
OWNR
ur
ms
bba
bb
bs
22
6
8
72
5
JPNR
OWNR
38
15
6
104
13
ur
ms
bba
bb
bs
19
14
31
270
15
P=0.373
Table C.6
JPNR
26
18
24
324
18
P=0.596
Frequency distribution of impact modes across height classes - study sites
pooled, ‘new’ and ‘old’ impact compared.
ur=uprooting, ms=mainstem breakage, bba=breaking of larger branches in order to
access smaller plant parts, bb=branch breaking, bs=bark-stripping
height
class
1
2
3
4
5
6
7
8
9
10
SUM
ur
new'
0
0
5
12
6
5
9
7
6
10
60
old'
0
0
2
5
3
3
4
2
1
25
45
new'
0
0
1
1
1
1
5
0
4
8
21
ms
old'
0
2
2
4
2
5
1
4
2
10
32
new'
0
0
0
0
0
1
0
0
1
12
14
bba
old'
0
0
1
3
1
2
1
3
0
44
55
7
new'
0
0
12
37
31
24
29
6
10
27
176
bb
old'
2
15
68
138
124
67
58
36
23
63
594
bs
new'
0
0
0
0
0
0
1
1
1
15
18
old'
0
0
0
2
1
0
2
1
2
25
33
no impact
ne'
old'
4
9
31
41
100
87
155
133
106
92
67
54
54
49
48
39
31
32
80
79
676
615
Appendix C
Table C.7
Sum of impact mode frequencies - study sites pooled, ‘new’ and ‘old impact
compared (chi-square test, χ2 = 61.752, df = 4, P = <0.001).
ur=uprooting, ms=mainstem breakage, bba=breaking of larger branches in order to
access smaller plant parts, bb=branch breaking, bs=bark-stripping
ur
ms
bba
bb
bs
Table C.8
new
old
60
45
21
32
14
55
176
594
18
33
P=<0.001
Frequency distribution for ‘degrees of intensity’ and their proportional share
for accumulative ‘old’ (a) and ‘new’ (b) impact - study sites compared and
pooled.
ur=uprooting, ms=mainstem breakage, bba=breaking of larger branches in order to
access smaller plant parts, bb=branch breaking, bs=bark-stripping
(a)
impact intensity
(ur+ms) heavy structural
change
(bba) heavy intensity
(bba) light to moderate
intensity
(bb) heavy intensity
(bb)light to moderate intensity
(bs) heavy intensity
(bs) light to moderate intensity
No Impact
OWNR
JPNR
%OWNR
% JPNR
pooled
%pooled
33
7
44
7
5%
1%
6%
1%
77
14
6%
1%
24
50
220
5
10
288
17
46
278
1
17
327
4%
8%
35%
1%
1%
45%
2%
6%
38%
0%
2%
44%
41
96
498
6
27
615
3%
7%
36%
0%
2%
45%
(b)
impact intensity
(ur+ms) heavy structural
change
(bba) heavy intensity
(bba) light to moderate
intensity
(bb) heavy intensity
(bb)light to moderate intensity
(bs) heavy intensity
(bs) light to moderate intensity
No Impact
OWNR
JPNR
%OWNR
% JPNR
pooled
%pooled
28
2
53
4
8%
1%
9%
1%
81
6
8%
1%
6
31
41
3
2
260
2
23
81
10
3
416
2%
8%
11%
1%
1%
70%
0%
4%
14%
2%
1%
70%
8
54
122
13
5
676
1%
6%
13%
1%
1%
70%
8
Appendix C
Table C.9
Frequency distribution of plant part utilization as share of total diet
- study sites compared (chi-square test, χ2 = 2.208, df = 4, P = 0.698).
plant part
roots
branches
bark trees
bark grewia
other (ms+bba)
OWNR
JPNR
22
38
31
52
5
13
41
52
14
21
P=0.698
(a)
(b)
12%
(21)
19%
(22)
12%
(14)
22%
(38)
roots
roots
branches
bark trees
36%
(41)
branches
30%
(52)
bark trees
bark grewia
bark grewia
other
4%
(5)
Figure C.9
27%
(31)
7%
(13)
Proportional share of plant part utilization within the total diet of
elephant for OWNR (a) and JPNR (b), with the frequency values in
brackets.
9
30%
(52)
other
Appendix D
Appendix D – Chapter 5
Table D.1
Availability (Pearson correlation, P = <0.001) and Acceptance (Pearson
correlation, P = 0.32 with Sbir, P = 0.007 without Sbir) indices for the eight
core species - bull groups versus family units.
bulls
AI
0.88
0.58
0.26
0.30
0.39
0.19
0.15
Species
Grewia
Capi
Anig
Dcin
Comi
Aeru
Aexu
Sbir
Pearson correlation
Table D.2
families
AI
0.91
0.50
0.24
0.39
0.30
0.22
0.20
0.18
0.09
P=<0.001
bulls
families
SA
SA
0.54
0.74
0.33
0.26
0.37
0.27
0.50
0.39
0.31
0.14
0.43
0.40
0.18
0.10
P=0.007
0.92
0.30
P=0.32
Height class acceptance values (utilized plants within a height class in relation
to the total number of utilized plants) - bull groups versus family units
(Pearson correlation, rs = 0.827, P = 0.003).
height class
acceptance values
bull
family
groups
units
0.00
0.00
0.00
0.01
0.09
0.06
0.25
0.21
0.16
0.12
0.08
0.15
0.12
0.19
0.08
0.05
0.05
0.09
0.17
0.13
rs = 0.827, P = 0.003
height
class
1
0-0.5m
2
0.5-1m
3
1-1.5m
4
1.5-2.0m
5
2.0-2.5m
6
2.5-3.0m
7
3.0-3.5m
8
3.5-4.0m
9
4.0-4.5m
10
>4.5m
Pearson correlation
10
Appendix D
Table D.3
Rainfall data 1985-2013, OWNR, provided by G.Reinstorf
Yellow indicating the overlapping period of available records for OWNR and JPNR
Green indicating records between 2009 and 2013
Year
1985
1986
1987
1988
1989
1990
1991
1992
1993
1994
1995
1996
1997
1998
1999
2000
2001
2002
2003
2004
2005
2006
2007
2008
2009
2010
2011
2012
mean
2013
Jan
136
48
72
20
12
93
80
21
46
65
121
76
88
52
74
68
3
46
23
107
111
119
64
53
123
81
166
291
88
296
Feb
107
0
5
78
82
28
49
8
21
28
17
236
93
0
108
279
163
2
18
83
4
184
26
0
38
0
35
0
60
Mar
107
16
64
125
56
42
82
46
86
21
49
68
0
22
48
131
52
18
57
65
24
54
21
19
68
12
8
23
49
April
0
126
6
2
11
27
11
4
13
14
58
29
27
14
18
38
53
33
4
62
39
0
24
15
8
131
73
0
30
May
28
0
8
0
11
0
5
0
11
0
0
55
19
0
0
16
15
0
0
0
0
0
0
0
30
0
35
0
8
June
12
0
4
0
21
0
9
0
0
0
0
0
0
0
0
18
1
7
2
0
0
0
0
0
5
0
1
0
3
July
4
0
0
0
0
0
0
0
0
0
0
27
0
1
8
0
0
0
0
0
0
0
16
0
5
3
0
0
2
11
Aug
0
8
36
2
5
4
0
5
0
0
0
22
5
2
5
0
0
0
0
0
0
0
0
0
0
0
0
0
3
Sept
0
2
39
2
0
0
0
7
0
6
0
10
16
7
0
0
0
11
9
0
0
0
42
0
0
0
0
25
6
Oct
61
22
36
41
53
55
0
1
7
51
31
38
35
34
75
15
51
15
23
11
5
11
31
3
0
11
95
76
32
50
Nov
92
37
30
8
134
30
63
49
57
20
59
63
95
108
84
152
171
5
29
105
99
107
64
88
140
73
43
26
73
17
Dec
151
61
142
71
123
99
24
137
150
102
166
43
12
58
42
92
109
57
16
51
21
60
134
81
67
89
52
69
81
104
Total
698
320
442
349
508
378
323
278
391
307
501
667
390
298
462
809
618
194
181
484
303
535
422
259
484
400
508
510
440
467
Appendix D
Table D.4
Rainfall data 1968-2013, JPNR, provided by G.Thomson
Yellow indicating the overlapping period (1985-2013) of available records for OWNR and JPNR
Green indicating records between 2009 and 2013
Year
1968/69
1969/70
1970/71
1971/72
1972/73
1973/74
1974/75
1975/76
1976/77
1977/78
1978/79
1979/80
1980/81
1981/82
1982/83
1983/84
1984/85
1985/86
1986/87
1987/88
1988/89
1989/90
1990/91
1991/92
1992/93
1993/94
1994/95
1995/96
1996/97
1997/98
1998/99
1999/00
2000/01
2001/02
2002/03
2003/04
2004/05
2005/06
2006/07
2007/08
2008/09
2009/10
2010/2011
2011/2012
2012/2013
2013/2014
Vienna/JPNR
Long term
average 1969
- 2013
July
Aug
RECORDS NOT
AVAILABLE
7
0
6.5
1
5
45
Sept
6.6
10.5
136
0
82.5
31.5
4.5
2.5
3.8
36
15.5
12
0.3
27.2
16
94.4
11.5
24.5
2
3
73.5
25
4
22
2.3
0
6.2
2.4
1
10.4
3.5
22.8
3
1
8
4.8
0
5.2
0
0
9.3
0
6
21
4.1
1.1
0
0
17.2
6.7
1.6
4.7
Oct
Nov
Dec
Jan
Feb
Mar
April
May
June
Total
15.5
96.5
20.5
94
40
33.8
46
13.5
17.5
6
45.2
77.5
42.5
51.5
0
37.2
92.5
51.7
20
37.5
29
61
45.3
99.5
72
53
142
55
133
36
120.5
28.5
136.5
46.5
77.8
1.5
57.5
38
132.6
17.5
105.8
151.5
193.5
161
95.2
139.5
0
173.6
219.4
62.8
135.8
53
14.4
104
31.4
110.8
92.9
36.4
44.6
55.2
38.2
87.6
119.6
96.7
93.1
92.5
131.1
124.3
137.9
134
264
172.5
54
64
140.5
319
145.5
168.7
73.8
42.5
183
140
50
95.8
150.1
66.5
47
25.5
0
29
59
74.5
48
40.3
160
170.5
72
59.8
37.2
90
26.6
39
40.9
105.9
95
118.7
38.5
53.1
209.1
98.9
132.8
409.3
254
113.6
8
27.5
19
197.5
26
42
86.5
174.5
159.5
115
55.5
124.8
96.5
7.5
0
19.7
154
10.5
6.5
84.5
102.5
39
99
2.7
16.7
19.2
25
258.8
137
8
107.5
240.4
120.4
38.7
18.5
95.9
4.5
236.7
26.7
32.1
43.3
2.3
29.6
86.5
13
62
180
0
42
0
94.5
78.5
109
53.2
48
21
13
74
84
95
35
104
39
68.5
55.5
51.5
0
34.9
61.1
61
45.7
18
30.2
35.2
27.6
28.2
21.7
78.5
98.2
66.9
84.7
53.4
27.5
92.8
23.4
4.8
65.3
38.3
0
1.5
100
2.8
7.6
18
0
8.5
69
0
11.5
22.5
7.5
33
42
16
3
48
9
11
20
41
0
0
17.8
12.9
61.6
0
1.5
20
25.8
0
0
5
10.3
0
17
6.5
0
22.5
4
10
37.1
81.7
38.9
71
19.5
47
19
45.1
20
67
29
58.6
53
85
59.5
140.1
106
16.5
190.2
69.3
87.8
51.7
30.5
54
128.7
25.6
84
57.8
59.7
29.6
78.5
45
100.8
159.6
93.7
111.5
140.1
7.4
35
78.9
99
114
71.3
96.8
104
82.2
92.2
35.9
78.4
37
0
57.1
92.6
29.6
34.1
2.9
69.6
26.4
16.6
43.1
30.6
12.9
122.7
53.6
19.3
111.9
414.5
267.6
606.5
834.6
227.7
493.8
428.5
769.8
557.1
687.7
377.2
487.9
542.6
471.5
274.5
591.8
740
405.3
423.7
519.5
520
449.4
423.9
161.2
388.3
427.9
465.4
809.4
453
266.6
569.7
653.2
451.9
424.9
247.6
475.3
344.3
609.7
388.7
414.4
604.8
454.8
481.1
759.1
672.5
377.9
36
72.3
89.1
106.6
69.3
53.4
29.4
4
15.3
46.8
52.5
46
25.6
62
43.9
10
56.1
23.4
15.3
8.8
9.8
14.7
69.8
12.9
10.4
4.8
26.4
9.1
4.9
3
12
12
10
45
31
2.5
27
7
4
10
8.5
11
18
40
9.2
6.3
64.6
8
0
3
12.4
14.8
2.3
14.6
2
2
2
0
12.6
0
0
7.8
4.1
2.4
3.6
33.3
7
75.6
10.6
3.1
487.3