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Environmental Information Service, Namibia for the Ministry of Environment,
Forestry and Tourism, the Namibian Chamber of Environment and the Namibia
University of Science and Technology.
The Namibian Journal of Environment (NJE) covers broad environmental areas of ecology, agriculture, forestry,
agro-forestry, social science, economics, water and energy, climate change, planning, land use, pollution, strategic
and environmental assessments and related fields. The journal addresses the sustainable development agenda of
the country in its broadest context. It publishes four categories of articles: Section A: Research articles. High
quality peer-reviewed papers in basic and applied research, conforming to accepted scientific paper format and
standards, and based on primary research findings, including testing of hypotheses and taxonomical revisions.
Section B: Research reports. High quality peer-reviewed papers, generally shorter or less formal than Section
A, including short notes, field observations, syntheses and reviews, scientific documentation and checklists.
Section C: Open articles. Contributions not based on formal research results but nevertheless pertinent to
Namibian environmental science, including opinion pieces, discussion papers, meta-data publications, non-
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including collections of related shorter papers like conference proceedings.
NJE aims to create a platform for scientists, planners, developers, managers and everyone involved in promoting
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ISSN: 2026-8327 (online). Articles in this journal are licensed under a Creative Commons Attribution-Non
Commercial-NoDerivatives 4.0 License.
Chief Editor: K STRATFORD
Editor for this paper: K STRATFORD
SECTION A: RESEARCH ARTICLES
Recommended citation format:
Sterk M, Santana Cubas F, Reinhard B, Reinhard F,
Kleopas K, Jewell Z (2023) The importance of large pans and
surrounding bushveld for black rhino (Diceros bicornis ssp.
bicornis
) habitat use in the Kalahari: implications for
reintroduction and range expansion. Namibian Journal of
Environment 7 A: 1–13.
Cover photo: A Robertson
Namibian Journal of Environment 2023 Vol 7. Section A: 1–13
1
The importance of large pans and surrounding bushveld for black rhino
(Diceros bicornis ssp. bicornis) habitat use in the Kalahari: implications for
reintroduction and range expansion
M Sterk1, F Santana Cubas1, B Reinhard2, F Reinhard2, K Kleopas3, Z Jewell4
URL: https://www.nje.org.na/index.php/nje/article/view/volume7-sterk
Published online: 7th September 2023
1 Amakali Conservation, Cologne, Germany. max.sterk7@gmail.com
2 Kuzikus Wildlife Reserve, Omaheke, Namibia
3 PO Box 62278, Windhoek, Namibia
4 WildTrack Inc., Duke University, Durham, NC, USA
Date received: 29th May 2023; Date accepted: 3rd August 2023.
ABSTRACT
In the Kalahari region of southern Africa, recurrent droughts can affect local livestock production and even lead to the loss of
traditional farmland. As a result, the wildlife economy has grown in importance as a profitable approach to the sustainable use
of native game species adapted to these challenging climatic conditions. This has led to restoration efforts in the region that
have brought back wildlife including the critically endangered black rhino (Diceros bicornis). To understand the
interrelationship between a reintroduced black rhino population and a rural Kalahari wildlife reserve, this research project
aimed to decode the key drivers of black rhino habitat use based on a multiscalar approach of combined aerial and ground
information on ecogeographical variables (vegetation and artificial habitat components) together with spatial rhino location
and individual movement data. On average, black rhino home ranges were found to be 67 ± 20 km2, with core areas of
24 ± 11 km2. These are predominantly covered by the landscape types of bushveld and calcareous pans. Analysis of the
different landscape factors present in the reserve showed that vegetation heterogeneity, vegetation density, vegetation damage,
browse availability and waterhole density were significantly higher in the pooled core areas of the total population compared
to less frequented areas. Furthermore, a binary logistic regression model predicted that browse availability and vegetation
heterogeneity of medium to large woody species to be the most significant effect on black rhino habitat use. The model also
showed a negative correlation with Acacia spp. saplings, which can be explained by the decline or absence of saplings in the
core areas due to the continuous feeding pressure of black rhinos and other herbivores. Evaluation of black rhino habitat use
and spatial distribution indicates a strong preference for the mosaic of microhabitats around calcareous pans and surrounding
lunette dunes covered by bushveld. Together with the year-round availability of water (rain-fed lakes and artificial waterholes),
these focal points are of high ecological importance and provide suitable habitat conditions that may highlight the potential
for further black rhino reintroduction and range expansion, as well as general rewilding efforts in the region.
Keywords: biodiversity, browse, bushveld, carrying capacity, drought, ecogeographical variables, home range, Namibia,
rewilding, rhino conservation, spatial distribution, vegetation, wildlife economy
INTRODUCTION
Over the past century, the translocation of wild
animals has become an important tool for managing,
restoring and enhancing declined populations
(Langridge et al. 2020). Translocated animals must
adapt to a new environment and quickly establish
natural behavioural patterns, which are part of the
acclimatisation process (Mazess 1975, Göttert et al.
2010). The conservation of the black rhinoceros
(Diceros bicornis) is a good example of how
translocations of individuals and small populations
have helped expand the species into its former range
and increase overall population numbers
(Göttert et al. 2010).
With the establishment of rhino sanctuaries
throughout the African continent, rhinos can be
reintroduced into protected areas where they have
gone locally extinct in the past. For several decades,
translocation has been a common practice, taking
place from high-risk areas and government lands to
private lands (Emslie & Brooks 1999). In comparison
to large state-owned national parks, such sanctuaries
can be found in established private wildlife reserves
or game farms which are safeguarding other wild
animals in a confined area. In some cases, the
protective attributes of these sanctuaries outweigh
their habitat suitability (Adcock et al. 1998, van der
Heiden 2005). As an example of such range
expansion projects, Namibia’s Black Rhino
Custodianship Programme (BRCP) is a rhino
conservation success story built on nationwide rhino
sanctuaries, spread across 10 communal
conservancies and 25 freehold ranches. It also
embodies several aspects of effective ecological
population management in line with international
guidelines (Kötting 2020, Muntifering et al. 2023).
Namibian Journal of Environment 2023 Vol 7. Section A: 1–13
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Conservation of the black rhino in Namibia faces
chronic challenges, i.e., despite an ongoing poaching
crisis, some local populations continue to exceed the
carrying capacity of conservation areas. Maintaining
and expanding this conservation programme is
expensive, and generating sufficient revenue is a
challenge (Kötting 2020). While many privately
owned areas in the central and northern regions of
Namibia are already part of the programme, other
areas are becoming increasingly important for
potential reintroduction and range expansion. An
example of this is the Kalahari region in central-
eastern Namibia, which has received limited
attention from the conservation programme to date,
despite representing a large proportion of Namibia’s
land area (Kötting 2021). Precolonial historical
records indicate that both species of rhino were once
common in central-eastern Namibia (today’s
Omaheke region, part of the Kalahari ecosystem),
making the Kalahari an important refuge for both
rhino and many native wildlife species (van Rooyen
et al. 2008, Wallgren et al. 2009, Sullivan et al.
2021).
A principal geomorphic feature of this semiarid
landscape is depressions or pans, which vary in size
and which are scattered throughout the entire region.
The pans are important temporary water reservoirs
during the rainy season and are characterised by
relatively high mineral content and, in some cases,
perennial grass cover (Lancaster 1974, Parris & Child
1973). Wind erosion deposits sediment from the pans
into the surrounding area to form flanking lunette
dunes (Haddon 2005). The pans and their
surroundings contain a high diversity of vegetation
and landscapes, are critical for wildlife species, and
are particularly selected and used for keeping
livestock from nearby settlements (van Rooyen &
van Rooyen 1998, Parris & Child 1973). As many
parts of the central and southern Kalahari have been
converted to pastoralism, human activities such as
overgrazing by livestock have had a negative impact
on vegetation conditions around the pans, resulting in
bush encroachment and reduced amounts of
perennial grasses and plant litter (Parris & Child
1973, Moleele & Mainah 2003, Wallgren et al.
2009).
The Kalahari is affected by extreme weather events
such as recurrent droughts, which threaten the
livelihoods of local communities and lead to
increased livestock mortality, crop failure and even
loss of farmland (Mogotsi et al. 2013). Although
droughts have occurred throughout history, ongoing
climate change is accelerating and amplifying these
events, leaving poorer households with limited
resources to adequately cope and adapt (Mogotsi et
al. 2011, 2013). A common consequence is an
increase in internal displacement and migration
(Adaawen et al. 2019).
The combination of such critical environmental,
socioeconomic and climatic factors is encouraging a
rethink of land use patterns in many regions of
Africa, with an increased emphasis on the sustainable
use of wildlife, which is more adaptable to
challenging site factors than traditional livestock.
The wildlife economy is a diverse sector that
combines ecotourism, the sale of live animals,
various forms of hunting and meat production (Child
et al. 2012). This profitable approach has extended to
the Kalahari, enabling rewilding efforts to restore
ecological balance and promote biodiversity,
particularly for threatened and keystone species such
as the black rhino. As an example, Kuzikus Wildlife
Reserve (KWR), a former cattle farm negatively
affected by decades of livestock grazing, has been
transformed into a wildlife sanctuary, with more than
40 years of ecological restoration. The reserve’s main
source of income is ecotourism, but it is also a
representative site for the BRCP, providing suitable
conditions for analysing habitat use in the Namibian
Kalahari and investigating the ecosystem’s value for
black rhino reintroduction and range expansion.
KWR was approved as one of the first reintroduction
sites under the programme in the late 1990s and over
the past 25 years the population has grown
remarkably. Its high population growth rate of 9%
lies above the Namibian BRCP average of 7.9% (net
of translocation) and 8.5% (net of translocation and
poaching), as well as the IUCN benchmark of 5%
(Emslie et al. 2019, Sullivan et al. 2021, Reinhard &
Reinhard 2022).
Several studies have addressed the issue of black
rhino habitat use, resource selection, spatial
distribution and habitat suitability assessment, testing
different methods and models. One of the recent
studies from 2015 used random forest models to
predict habitat use (Lush et al. 2015), another from
2012 focused on logistic regression and Bayesian
Information Criterion (Buk 2012), Simon Morgan
(2010) included a maximum entropy (Maxent) model
and van der Heiden (2005) worked with a utilisation
distribution. As the first black rhino home range and
habitat use study of its kind in this landscape and
ecoregion of Namibia, it is important to understand
the full picture of how specific ecogeographical
variables (EGVs) of the Kalahari ecosystem, as well
as species–habitat interactions, affect the spatial
distribution of a reintroduced black rhino population.
This, in turn, may help to maintain viable
populations, improve local management strategies
and even promote further conservation efforts in the
Kalahari ecosystem (Göttert et al. 2010, Morgan
2010).
METHODS
Study site and landscape
The study took place in the Kuzikus Wildlife Reserve
Namibian Journal of Environment 2023 Vol 7. Section A: 1–13
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which is located approximately 150 km southeast of
the Namibian capital, Windhoek, at 23°16’–23°26’S
and 18°33’–18°48’E. The private reserve covers
around 115 km2, lies about 1,350 m above sea level
and is mostly flat, except for three dunes of about
15 m in height. It is surrounded by a perimeter fence,
and the lodge and staff village have an additional
interior fence. Ecotourism has been active in the
reserve since 2005 and includes regular human
activity, mostly focused on guest-related activities
such as game drives, nature walks and horse riding,
as well as management activities such as road,
waterhole and fence maintenance, anti-poaching
patrols, and occasional game management and
ecosystem drone mapping operations.
KWR can be divided into six different vegetation
types, comparable to those of the Kalahari Gemsbok
National Park in South Africa (now part of the
Kgalagadi Transfrontier Park) (van Rooyen et al.
2008, Sterk 2019). The area belongs to the southern
Kalahari, part of the Acacia Tree-and-Shrub Savanna
biome (Figure 1) (Atlas of Namibia Team 2022). The
most common vegetation type, the low duneveld,
occupies 46% of the reserve and is characterised by
Acacia erioloba and Schmidtia kalahariensis. In
contrast, the bushveld covers almost the other half of
the landscape and is dominated by different Acacia
spp., mainly Acacia mellifera. As the bushveld
appears to vary locally, two parameters (bush density
and dominant species present) were used to
differentiate bushveld, resulting in three distinct
bushveld types (mixed bushveld, open A. hebeclada
bushveld and dense A. mellifera bushveld).
Depressions are found in 12 calcareous pans, two of
which are large (1.9 and 0.8 km2). The sandy
grassveld forms the landscape between the high
dunes and contains a very low proportion of woody
vegetation.
Previous study
The home range and habitat use analysis follows up
on a previous study on the carrying capacity of black
rhinos in the KWR from 2018 to 2019, and integrates
its findings on how habitat and food resources limit
population growth. For KWR, the Southern African
Development Community’s Rhino Management
Group (SADC RMG) Black Rhino Carrying
Capacity model v.2 predicts a total browse
availability score of 7.95% and a mean ecological
carrying capacity estimate of 11 black rhinos on the
115 km2 property (Table 1) (Sterk 2019). Black rhino
browse availability (BA) is defined as the landcover
which describes the percentage of available food
plants in a three-dimensional space between 0 and
2 m height (Adcock 2017).
Study population and other herbivores
During the period of data collection, ten individual
black rhinos were present in the reserve; two
territorial bulls, two subadult bulls and three adult
cows, each accompanied by one calf. Each individual
was given a name and all adult animals have specific
ear notches to facilitate identification. The reserve
Figure 1: Map of the Kalahari landscape (Namibia) showing the location of Kuzikus Wildlife Reserve within the Kalahari, the
extent of the
Acacia Tree-and-
Shrub Savanna, characteristic landscape features and conservation/wildlife areas (Atlas of
Namibia Team 2022).
Namibian Journal of Environment 2023 Vol 7. Section A: 1–13
4
also hosts several species of browsing herbivores that
compete with black rhino, including Angolan giraffe
(Giraffa giraffa angolensis), common eland
(Taurotragus oryx), greater kudu (Tragelaphus
strepsiceros), gemsbok (Oryx gazella), common
impala (Aepyceros melampus), springbok
(Antidorcas marsupialis), common duiker
(Sylvicapra grimmia), and steenbok (Raphicerus
campestris).
Data collection
Home range studies, as well as understanding habitat
use, require a large number of location points from
individual black rhinos for statistical analysis. This is
typically achieved using VHF and GPS collars (horn
implants or anklets) (Seidel et al. 2019). Darting and
collaring rhinos is a costly operation involving
helicopters, vets, ground staff, drugs, and technical
equipment, and is also stressful for the rhinos
(Morkel 1994). To avoid such events, a mix of
sampling methods was used in this study.
Georeferenced location data points were collected
over a period of ten months (between June 2021 and
April 2022). Data collection consisted of direct field
observations, via identified footprints, evaluated
night-vision camera trap images, operational drone
flights for anti-poaching and ecosystem mapping,
and external data points from other reserve staff.
The approach to visually locate individuals and map
corresponding footprints was based on a stratified
random survey method, with the reserve divided into
four zones. Within each zone, vehicle and random
off-road foot patrols were conducted to monitor rhino
activity. Zones were then rotated on a daily basis,
with the reserve’s road network allowing rapid access
to all areas. In open areas, rhinos could be spotted at
distances of more than 1 km. Random foot patrols
were also conducted frequently to access areas of
dense vegetation with limited visibility, looking for
rhino tracks and signs. Two night-vision camera traps
were placed at waterholes, salt rocks, dung middens
and rhino ‘highways’, rotating between these sites
every 3–4 days, while an additional six camera traps
were placed at random locations within the reserve
and moved to a different site each week. To avoid
bias caused by frequent rhino activity around
waterholes, information on their location was placed
on identified trails that were at least 250 m away from
the waterholes.
To link rhino footprints to individuals, each rhino was
tracked at least once at the beginning of the study to
obtain clear footprint images in bare substrate,
resulting in a verified identification catalogue.
Subsequently, when rhino tracks were found,
footprint identification was based on visual
comparison of heel line patterns, which are unique to
each rhino. This non-invasive approach can be
reliable with high accuracy in small populations
(Jewell et al. 2020).
Sightings were added into the database if separated
by at least one day, implying that information on each
individual rhino could only be recorded once a day.
Nearly all activities were conducted either in the
early morning or late afternoon hours, as most of the
rhinos were active at these times. All rhino related
data include information on date, time, ID of the
individual, its behaviour, whether there was a change
in behaviour caused by the observation and the
method used for data collection. To map rhino
locations, GPS data points were logged on the
ArcGIS Explorer App for IOS (Esri Inc. 2018–2020).
Habitat use was determined taking different EGVs
into consideration. Based on literature review, the
following variables were chosen for their important
role in rhino habitat use and preference: ‘browse
availability’ (related to the vegetation type);
‘vegetation density’; ‘vegetation heterogeneity’;
‘vegetation damaged by rhinos’; ‘Acacia spp. sapling
distribution’; ‘availability and distance of permanent
water points’; ‘intensity of road use’ (van der Heiden
2005, Morgan 2010, Buk 2012, Lush et al. 2015).
In November 2021, as part of the Kuzikus Mapping
Project (see https://kuzikus-namibia.com/research), a
two-week drone mission using a SenseFly eBeeX
fixed-wing aircraft, flown at an altitude of 160 m,
collected 3 cm high-resolution RGB imagery to build
an aerial imagery database of the entire reserve. The
Table 1: Average black rhino browse availability values for each vegetation type found in Kuzikus Wildlife Reserve expressed
as the percentage of land cover. Analyses were done using the black rhino carrying capacity model v2 (Sterk 2019).
Low
duneveld
Mixed
bushveld
Dense
bushveld
Open
bushveld
Sandy
grassveld
Pan
Average browse availability 5.7% 9.7% 12.4% 1.2% 0.2% 0.9%
Percentage of land area 46.0% 32.0% 17.5% 1.1% 0.5% 2.9%
Vegetation type contribution to
total browse availability score
2.622% 3.117% 2.167% 0.013% 0.001% 0.027%
Total browse availability score for Kuzikus Wildlife Reserve: 7.95%
Namibian Journal of Environment 2023 Vol 7. Section A: 1–13
5
mission was timed to capture medium to large
vegetation at the end of the dry season and just before
the first rains of the wet season, allowing optimal
visualisation and accurate delineation of landscape
and vegetation components. These high-resolution
drone images, supplemented by publicly available
satellite data and validated through a ground-truthing
process, were used to map all distinctive landscape
features, vegetation types, their densities and
artificial elements (waterholes, fences, buildings and
roads) within the reserve’s infrastructure. These
digitised features were then integrated into ArcGIS to
create a geospatial ‘digital twin’ with defined
boundaries of the different features (Esri Inc. 1999–
2019).
During the same period, a vegetation survey of the
reserve was carried out. For this purpose, a digital
grid consisting of square grid cells measuring 750 m
x 750 m was created over the study area using
ArcGIS. A total of 260 cells were generated, and the
midpoint of each cell was marked as the location for
establishing vegetation plots (Esri Inc. 1999–2019).
Consequently, the vegetation data obtained from
each plot represented the corresponding grid cell.
Along the reserve boundary, plots were positioned as
centrally as possible.
The 50 m diameter vegetation plots were used to
manually record key aspects of the vegetation,
consisting of the vegetation heterogeneity (number of
woody species), the extent of damage caused by
black rhino browsing (expressed as a score) and the
presence of Acacia spp. saplings (counted
individuals). In addition, browse availability values
derived from data from the previous carrying
capacity study were assigned to the different
vegetation types found in the reserve. These relative
values were also integrated into the corresponding
grid cells.
On completion of the plot survey, the response data
for each vegetation variable was divided into four
categories (absent/very low, low, medium and high)
and assigned accordingly, resulting in a scorecard.
This helped to better visualise the data in the next step
(Table 2).
For the artificial habitat features, the availability,
distances and densities of water points were assessed
using the digital twin, while road transects were
assigned to a specific category indicating the
frequency of weekly use. Subsequently, these data
were also integrated into the grid cell database.
Data analysis
Based on the rhino location points, individual home
ranges were estimated and combined to a pooled
population model using the Kernel Utilisation
Distribution (KUD) estimation in R with the package
‘adehabitatHR’ (Calenge & Fortmann-Roe 2015).
When examining habitat use, the characteristics of
the core ranges were particularly considered, which
are salient areas that include 50% of all the nearest
location points, demonstrating a direct preference for
the area (Lent & Fike 2003).
Chi-squared and Fisher’s tests were applied to assess
significant differences between vegetation types
within the core and peripheral areas of the rhino
range. In addition, analysis of variance (ANOVA),
T-tests and Tukey tests were used to determine
significant differences in the distributions of
vegetation heterogeneity, vegetation density,
damaged vegetation, waterhole density, waterhole
distance and road transect (use categories) between
the core and peripheral areas (*** p ≤ 0.001; **
p ≤ 0.01; * p ≤ 0.05).
To evaluate the interaction of EGVs and their impact
on rhino habitat use, multicollinearity between the
single variables was calculated using a variance
inflation factor (VIF). A value of 1 indicates no
correlation, a value between 1 and 3 indicates a
moderate correlation and values > 3 represent strong
correlations and can be excluded as coefficient
estimates, while p values in the regression output are
likely to be unreliable (R Core Team 2018, Statology
2021).
Secondly, a binary logistic regression model
(BLRM) and the odds ratio of each variable was
calculated using the grid cell database combined with
the response variable ‘core area’ (‘Yes’ or ‘No’)
(Harrell 2015).
Table 2: Ecogeographical variables scorecard by category per 50 m diameter vegetation plot, including vegetation damage
(score), vegetation density (number of trees and bushes), vegetation heterogeneity (number of woody plant species),
Acacia
spp. saplings (number of saplings), browse availability (score) grouped into to four categories (absent/very low to high).
Category
Vegetation
damage
Vegetation
density
Vegetation
heterogeneity
Acacia spp.
saplings
Browse
availability
(score)
(Number of trees
and bushes)
(Number of woody
plant species)
(Number of
saplings)
(score)
Absent/very low
0 – < 1
0
0 – 2
0
0 – < 3.25
Low
1 – < 3
< 20
3 – 4
1 – 3
3.25 – < 6.5
Medium
3 – < 5
20 – 40
5 – 6
4 – 6
6.5 – < 9.75
High
≥ 5
≥ 40
≥ 7
≥ 7
≥ 9.75
Namibian Journal of Environment 2023 Vol 7. Section A: 1–13
6
The model was checked in advance for its
meaningfulness. This included an omnibus test to
find out whether the test model makes a significant
explanatory contribution compared to the null model.
A chi-squared probability was identified for this and
checked if the R-squared value can be applied as a
goodness-of-fit measure for logistic regression
models using the Nagelkerke method (Nagelkerke
1991).
The odds ratio (OR) is a useful way of assessing the
likelihood of an outcome occurring given a particular
exposure. If the OR value is > 1, an increased
occurrence of the event can be expected. An OR
value < 1 indicates a decreased occurrence of the
event (Tenny & Hoffmann 2021). In terms of black
rhino habitat use, this means that if the categorical
value of each variable increases by one unit, the
probability of an area being considered a rhino core
area will either increase or decrease by the given OR
value.
RESULTS*
*Maps showing rhino locations, home ranges,
habitat use, or reserve infrastructure are withheld for
security reasons.
A total of 518 rhino location points were recorded
during the study. Direct observations provided 296
data points, 167 were derived from footprints, tracks
and signs associated with individual rhinos, 52 data
points are from camera traps and three individuals
were spotted during operational drone flights.
Due to the topography, the black rhino population has
access to almost the entire available area within the
perimeter fence, excluding the inner fenced reserve
infrastructure. The pooled spatial distribution of all
individuals covers 112 km2 (95% isopleth) and core
areas 60 km2 (50% isopleth). The following spatial
categories refer to the core and peripheral areas of the
home ranges. Unused areas are almost non-existent
and of no further significance.
The spatial distribution of the population is largely
based on the two separate home ranges of the two
dominant bulls, Columbus and Hermes. In contrast,
the home ranges of females and subadult bulls show
that they predominantly share the same areas, are
similar in size and fully overlap with the core area of
Columbus. On average, individual home ranges are
67 ± 20 km2 (95% isopleth) and core areas are
24 ± 11 km2 (50% isopleth) (Table 3).
Vegetation types vary significantly between the core
and peripheral areas of the pooled total rhino
population range (Fisher’s test: p < 0.0005***). The
core areas are dominated by bushveld types, which
occupy 70% of the area, while low duneveld occupies
25% and pans 5%. No sandy grassveld was recorded
in the core areas. On the other hand, low duneveld is
the most common vegetation type in the outer areas
of the pooled home ranges, covering more than 70%.
Mixed bushveld and dense bushveld cover
comparatively less land at 19% and 9% respectively.
Calcareous pans were not found to be used in the
peripheral areas (Figure 2).
During the spring season in September and October,
a shift in the range of female rhinos was observed. At
this time, they mainly visited the dune system.
During the remaining months of the study period, the
females remained in the bushveld areas of the
reserve. The 50% isopleth of their range was 18 km2
in spring and 12 km2 during the rest of the year. No
seasonal shift in the distribution of bulls was
observed.
Data analysis of the natural EGVs present in the
wildlife reserve showed vegetation heterogeneity
(p < 0.0001***), vegetation density (p = 0.0063**),
vegetation damage (p = 0.0004***) and browse
availability p = 0.0005***) were significantly higher
in the pooled core areas of the total population
compared to the less frequented peripheral areas. No
significant differences in the number of Acacia spp.
saplings were found between the two types of areas
(p = 0.240) (Figure 3).
Table 3: Home range size estimation (50% and 95% isopleths) for individual rhinos in the Kuzikus Wildlife Reserve (including
information on sex and year of
birth) and ratio of 50% isopleth size to 95% isopleth size. For females, calf names and year of
birth are shown in brackets.
ID adults
(and calves) Sex Year of birth
Sample
contribution
(n)
50% isopleth
(core area)
in km²
95% isopleth
(home range)
in km²
Ratio 50% to
95% isopleth
Columbus
male
1992
92
45
91
0.49
Hermes
male
2002
102
8.5
25
0.34
Hector
male
2016
68
25
71
0.35
Helia
(Hades)
female
(male)
2007
(2021)
71 22 64 0.34
Juno
(Jonas, Jakari)
female
(male, male)
2005
(2015, 2019)
110 18 67 0.27
Kenia
(Kauri)
female
(female)
2008
(2021)
75 23 69 0.33
Namibian Journal of Environment 2023 Vol 7. Section A: 1–13
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For the artificial habitat variables, we found that
waterhole density is significantly higher in the core
areas of the pooled rhino home ranges
(1.8 waterholes per 10 km²) compared to the
peripheral areas (0.6 waterholes per 10 km²)
(p = 0.0002***). However, there is no statistically
significant difference between the two area types in
terms of mean distance between nearby waterholes
(p = 0.091) and the frequency of road use (p = 0.065)
(Table 4).
Within the binary logistic regression, the variables
were first tested for multicollinearity. No values were
excluded from further interpretation as none
exceeded a value of 3. The highest correlation is
between vegetation density and browse availability
(r = 0.736). The correlation coefficient between
vegetation density and vegetation heterogeneity is
similarly high at 0.699. Vegetation heterogeneity
together with browse availability also have a
moderate correlation coefficient of 0.559. The
remaining coefficients are all below 0.5, indicating a
lower degree of correlation and even weak negative
correlations (Table 5).
Binary logistic regression model
Using an omnibus test, the chi-squared probability
was found to be significant for its explanatory
contribution compared to the null model
(p < 0.0001***). As a measure of goodness-of-fit for
logistic regression models, the Nagelkerke R-squared
value of 0.43 indicates a medium to strong
Figure 3: Effect of ecogeographical variables (EGV) (vegetation damage, vegetation density, vegetation heterogeneity, Acacia spp.
saplings and browse availability) on black rhino habitat use in the Kuzikus Wildlife Reserve.
Figure 2: Distribution of vegetation types within black rhino core areas and peripheral areas in the Kuzikus Wildlife Reserve.
Namibian Journal of Environment 2023 Vol 7. Section A: 1–13
8
relationship and corresponds to real world
phenomena (Nagelkerke 1991).
The BLRM results show vegetation heterogeneity
(p = 0.007**) and browse availability (p = 0.008**)
have the most significant effect on the utilisation of
the investigated areas as rhino core range (Table 6).
The number of Acacia spp. saplings also has a
significant but negative effect (p = 0.01*).
Odds ratio
Vegetation heterogeneity has the highest value
(OR = 2.2), which means that an area is 2.2 times
more likely to be used as a core area by black rhino if
woody plant diversity increases. The two categories
of vegetation density and browse availability would
also increase rhino occurrence by a factor of > 1.5.
The presence of waterholes and vegetation damage
by rhinos have lower values (OR = 1.3). However,
the intensity of road use and the number of Acacia
spp. saplings have negative OR values (Table 7).
Increasing the categorical values of these two
variables reduces the likelihood of areas being
classified as rhino core range.
DISCUSSION
Home ranges
Across the African continent, black rhino home
ranges vary widely, from small (3 km2) in humid and
subtropical areas to extremely large (300 km2) in arid
areas such as northwestern Namibia (Plotz et al.
2016). With an average annual rainfall of
approximately 210 mm, the Kuzikus Wildlife
Reserve lies in the middle range of Kalahari rainfall
(150–300 mm) (Wasiolka & Blaum 2010). Large
home range sizes would be expected in such an
environment, but the maximum distribution of
115 km2 cannot be exceeded as the perimeter fence
limits the space available to rhinos. The home range
sizes of rhino bulls vary considerably across the
reserve, reflecting similar findings from Hluhluwe-
Imfolozi Park in South Africa (Reid et al. 2007). At
62 ± 28 km2, the mean home range size of bulls is
also smaller than in other arid rhino habitats in South
Africa (Lent & Fike 2003). On the other hand,
females tend to have larger territories than bulls (Reid
et al. 2007), which is also somewhat evident here
(67 ± 2 km2).
Table 5: Matrix of correlation coefficient values between each ecogeographical variable and variance inflation factor.
Vegetation
density
Vegetation
heterogeneity
Browse
availability
Vegetation
damage
Saplings
count
Waterholes
Road use
intensity
Vegetation density
Vegetation heterogeneity
0.699
Browse availability
0.736
0.559
Vegetation damage
0.447
0.496
0.386
Saplings count
0.382
0.228
0.253
0.170
Waterholes
0.151
0.238
0.208
0.160
0.002
Road use intensity
0.129
0.191
0.170
0.043
0.051
0.134
Variance inflation factor
2.932
1.895
1.987
1.219
1.870
1.081
1.067
Table 6: Results of the binary logistic regression model including estimates, standard error, z and p values for each
ecogeographical variable.
Estimate
Standard error
z value
p value
Significance
(Intercept)
-4.152
0.864
-4.807
1.53e-06
***
Vegetation density
0.612
0.340
1.799
0.072
Vegetation heterogeneity
0.776
0.290
2.674
0.007
**
Browse availability
0.597
0.225
2.652
0.008
**
Vegetation damage
0.270
0.232
1.167
0.243
Saplings count
-0.652
0.256
-2.549
0.011
*
Waterholes
0.286
0.682
0.419
0.675
Road use intensity
-0.236
0.155
-1.519
0.129
Table 4: Waterhole distribution and road use intensity by
black rhino in the Kuzikus Wildlife Reserve.
Artificial habitat features
Core
areas
Peripheral
areas
Waterhole distribution
Density
1.8/10 km²
0.6/10 km²
Mean distance
3.2 km
3.5 km
Road usage (frequency)
Once a day or every second
day
10.2 km 6.8 km
Every 3–4 days
37 km
15.5 km
Less than once a week
31.5 km
39.7 km
Namibian Journal of Environment 2023 Vol 7. Section A: 1–13
9
The home range and carrying capacity analysis
suggests that there is insufficient space for more than
two dominant rhino bulls. This should be taken into
account in the population management and in the
BRCP to avoid increased bull mortality due to
excessive fighting. Therefore, it is recommended to
remove 2–3 subadult bulls from the reserve as soon
as they attain maturity and are no longer dependent
on their mothers.
Seasonal shift of home ranges
In large natural environments as well as in small,
fenced conditions, it is known that black rhinos and
other megaherbivores shift their spatial distribution
between dry and wet seasons (Shannon et al. 2006,
Reid et al. 2007). The observed spatial shift of the
females’ home ranges towards the dune system
coincided with the beginning of the Acacia blooming
during springtime. In the Kalahari, A. erioloba and
A. mellifera flower at the end of the cool, dry
wintertime and set fruit before the start of the rainy
season (Sekhwela & Yates 2007). In the months of
September and October, all rhino cows were
regularly found in this part of the reserve. During this
period, A. mellifera flowered profusely 3–4 weeks
before bushes elsewhere in the reserve. The reason
the Acacia bloom starts earlier on the dune crest is
unknown. A possible explanation might be the
increased amount of sunshine due to direct exposure
of the dune crests and less frost during the winter
compared to the dune valleys and plains. Although
rhino tolerance for more open vegetation increased
during this period, isolated islands of dense bush
providing cover and shade were still important as
they were frequently visited and used for daytime
resting.
Habitat use
The multiscalar overlay of spatial habitat and
vegetation information, in combination with the
defined home ranges, allows us to determine how
ecogeographical factors, the seasonal variation in
resources, and social interactions between the
individuals influence habitat use by the black rhino
population in the KWR. These results correlate with
the findings that herbivores typically respond
adaptively to spatial and temporal changes in
resource availability and suitability while
significantly redesigning their environment (Owen-
Smith 2010). In the Kalahari, suitable local
environmental conditions are found to be a
combination of high vegetation heterogeneity and
high browse availability, which are the strongest
predictors of rhino habitat use. This is particularly
evident around calcareous pans flanked by lunette
dunes and the surrounding bushveld, which create a
mosaic of microhabitats and form the core areas of all
individual home ranges. These focal points regularly
host the entire black rhino population at the same
time, making them highly valuable congregation sites
for socialising (van Rooyen et al. 2008).
Vegetation density, heterogeneity and browse
availability
When comparing the two prominent vegetation types
in the KWR, black rhino habitat use shows a clear
preference for bushveld, with the three types of
bushveld accounting for 70% of the core areas,
compared to 71% covered by low duneveld in the
peripheral areas of the home ranges. Here, the
bushveld areas can be attributed to the high browse
availability scores. In these areas, vegetation density
is also positively correlated with browse availability.
Although vegetation density was not identified as a
main driving factor in habitat use, dense bush thickets
or bush islands jutting out from more open
landscapes were often recorded as foraging and
resting sites, providing cover, shade and increased
browse availability. As also shown in the arid
northwest of Namibia, the intensive use of certain
areas by black rhino is directly related to browse
availability (Shivute 2008). In contrast, the large
calcareous pans make up only about 3% of the total
area and have a comparatively low browse
availability value due to their sporadic or low
vegetation height. Nevertheless, they are preferred
and frequently visited by rhinos. This can be
explained by the heterogeneity of the vegetation
which is associated with a higher species composition
of preferred browsable species (especially small
shrubs and herbs). These foraging areas contain
multiple microhabitats within a vegetation
community and are selected over other areas (Buk &
Knight 2010). Particularly during the dry season, this
may also have a positive effect on individual fitness
to compensate for the lack of nutrient intake when
favourable plant species become less available (Oloo
et al. 1994).
Damage to vegetation as a result of browsing
In the KWR, patches of vegetation with broken
branches or even trees and bushes that had been
completely toppled by rhinos were common. In
response to browsing pressure, field observations
suggest a different growth form for Acacia erioloba,
which is more horizontal (as a result of being pushed
by rhinos and continuing to grow) (Amanyanga
2017). It was also observed that isolated bushes or
Table 7: Odds ratio of each ecogeographical variable included in the binary logistic regression model.
Vegetation
density
Vegetation
heterogeneity
Browse
availability
Vegetation
damage
Saplings
count
Waterholes
Road use
intensity
Odds ratio
1.844
2.173
1.816
1.310
0.521
1.331
0.790
Namibian Journal of Environment 2023 Vol 7. Section A: 1–13
10
bushes in clusters surrounded by open areas showed
more damage than individuals within bush thickets.
The open space around the bushes could explain why
these bushes are more often targeted, as they are
easier to approach and feed on. It is difficult to
determine the extent to which rhinos affect plant
growth in the reserve. However, there is a significant
negative impact in certain areas and on certain plant
species. Rhinos usually feed on a wide variety of
plants, but often a limited number of species
contribute to most of the ingested biomass (Loutit et
al. 1987, Muya & Oguge 2000). This is consistent
with observations of rhino feeding behaviour in the
study area, while the increased feeding pressure on
specific woody plant species in the KWR confirms
the preference for Acacia species and Grewia flava
(Shaw 2011). These are complemented by
Catophractes alexandri, which is known to
contribute a large proportion of the diet throughout
Namibia, particularly in Etosha National Park
(Joubert & Eloff 1971, Curtis & Mannheimer 2005).
With key forage species under constant browsing
pressure, with no rest for regrowth and little chance
of survival, a long-term decline in browse availability
can be expected. This could have negative impacts on
black rhino population size and reproduction rates, as
suggested by a similar scenario with A. haematoxylon
in the southern Kalahari of South Africa (Shaw
2011). In order to adapt to the potential depletion of
key resources in fenced areas through increased
browsing pressure, possible measures could include
reducing herbivory by fencing off severely degraded
areas, managing black rhino numbers and other
browsing game species that directly compete with
them (Redick & Jacobs 2020). In particular, the
argument for increasing the range of black rhino
through land expansion should be considered.
Conversely, rhino impacts can also be positive at both
macro- and microhabitat levels. Like other
megaherbivores, black rhinos are considered to be
ecological engineers (Owen-Smith 1998). In
particular, through their feeding behaviour and
dispersal, black rhinos have great potential to alter the
structure of landscape vegetation. Observations in the
KWR have shown that a variety of other smaller
animal species benefit from the fallen branches or
toppled bushes. For instance, they provide new
hiding places and make leaves more accessible,
increasing browse availability for springbok,
common duiker and steenbok, amongst others
(Amanyanga 2017). Seed pod ingestion and
excretion in moist dung also aids seed dispersal and
germination; germination is often higher when seeds
have been previously ingested by herbivores (Miller
1995).
Acacia spp. saplings
At the landscape level, the distribution of the age
structure of Acacia erioloba in KWR is mostly
homogeneous. This means that in certain areas,
young and middle-aged individuals are absent while
the population continues to age. This picture clearly
stands out from that of the surrounding livestock
farms, where the tree population consists of a diverse
age structure. The absence or limited growth of
young saplings in the KWR can be attributed to the
impact of browsing herbivores. On livestock farms
herbivory is mostly through grazing rather than
browsing; this results in higher surviving rates of
saplings and in heterogenous tree populations.
Conversely, the risk of overgrowth and woody
encroachment is higher on livestock farms (Riginos
& Young 2007).
However, the absence of Acacia spp. saplings is
widespread throughout the entire reserve and does
not only occur in highly frequented rhino areas. A
density-dependent mortality among young Acacias
has been observed in other areas of the Kalahari, as
they often do not survive in direct resource
competition with similarly old individuals or with
dense grass cover (Skarpe 1991, Riginos & Young
2007). Additionally, an increased mortality in
middle-aged Acacias is also known in the region
(Moustakas et al. 2006).
The BLRM has shown a significant negative
correlation predicting habitat use of black rhinos.
This means that areas which are favoured as feeding
grounds have a low number of saplings. It can be
assumed that these areas contained higher numbers of
saplings in the past, which continuously decreased
over time due to feeding pressure of the rhinos and
other herbivore species and now result in the absence
or low amounts of surviving individuals. It is also
known that rodents as well as invertebrates can have
substantial impacts on the survival rates of saplings
(Riginos & Young 2007).
Artificial habitat components
No general avoidance of habitat use was observed in
areas frequently traversed by vehicles. In this context,
flight distances were recorded for individual rhinos,
limited to an average of 200 m and varying
considerably between individuals (from 0 to a
maximum of 1,200 m). Here, adjacent areas of dense
vegetation appear to have a positive effect on
reducing flight distances compared to more open
areas. In addition, it is still uncertain whether
increased human activity has a direct impact on black
rhino habitat use. In the KWR, contrasting scenarios
were observed. First, one habituated adult bull
showed minimal signs of avoiding human presence,
as evidenced by its frequent proximity to residential
structures. In contrast, all females and their calves
appeared to actively avoid human occupied areas.
However, this behaviour may also be influenced by
the less favourable habitat conditions in these areas.
Namibian Journal of Environment 2023 Vol 7. Section A: 1–13
11
It is well known that black rhinos usually drink daily
and often spend time at waterholes, especially at
night when social gatherings are common.
Waterholes therefore play an important role in the
social life of rhinos (Schwabe et al. 2015). They also
provide mud wallows, which are used for cooling
down the rhinos’ bodies and for skin care (Joubert &
Eloff 1971). However, no direct influence of
waterhole availability on rhino habitat use was found,
as the waterholes are well distributed and evenly
spaced across all regions of the reserve. It is possible
that the likelihood of waterholes being visited
regularly is affected by the surrounding suitable
habitat that can be used on the way to or from the
waterhole. In this context, it is worth reiterating the
importance of waterholes at calcareous pans in
conjunction with adjacent feeding areas, as they
provide suitable areas for general daily food,
minerals and water intake, as well as for social
interactions, which is presumably why these habitats
are used most frequently by all individuals. Artificial
waterholes constructed at the pans are therefore
essential to provide a constant supply of water during
the dry season, while abundant rain in the wet season
can flood the pans and create large lake systems.
CONCLUSION AND MANAGEMENT
RECOMMENDATIONS
Given the suitable conditions and landscape
characteristics for black rhino found around large
calcareous pans, these findings serve as a possible
explanation for the high population growth in KWR,
as well as for the eastern region of Namibia
(Muntifering et al. 2023). Successful rewilding
efforts in the region have been shown to restore
ecological balance and promote biodiversity,
especially for threatened species such as the black
rhino. With rewilding efforts, the Kalahari could
become an important base for the conservation of the
southwestern black rhino (Diceros bicornis ssp.
bicornis) population in the future.
When considering further reintroductions of black
rhino into the sparsely populated central or southern
Kalahari, sites with one or more large pans in
conjunction with surrounding belts of diverse and
dense vegetation should be favoured. The diverse
habitats and vegetation types around the pans can be
used to compensate for less suitable adjacent areas.
Alternatively, or in addition, riverine landscapes in
this region also contain a high diversity of vegetation
that could also provide suitable black rhino habitat
(van Rooyen & van Rooyen 1998). By prioritising the
restoration of natural processes of these characteristic
landscape features, abandoned or degraded farmland
containing pans and/or rivers may provide rewilding
opportunities, where black rhino reintroduction can
play an important role (Monbiot 2013). As natural
ecosystem engineers, black rhinos are critical for
ecosystem functioning. They can shape open
landscapes, reduce bush encroachment, transport
seeds and nutrients, and influence species
composition and carbon storage in ecosystems,
which in turn may benefit other native species and the
wildlife economy as a whole (Seidel et al. 2019).
Achieving this would require carefully considered
actions (e.g., management of natural resources and
reserve infrastructure, security measures, community
engagement), which could help manage existing
hazards, such as the high number of livestock fences
or the lack of adequate water points (Emslie &
Brooks 1999, Ferguson & Hanks 2010).
This information, together with the other suitability
parameters of the official assessment protocol, can be
used to evaluate sites for black rhino custodianship
applicants in the Kalahari. In particular, the habitat
objective can consider identified region-specific
conditions such as the presence, size and number of
pans and their surrounding vegetation heterogeneity
and characteristics (MEFT 2020). This may help
inform the decision-making process for assessing
future rhino conservation areas to further increase the
population and range of black rhino throughout the
region.
RESEARCH PERMIT
For this study, an official research permit
(RPIV01042024) was issued by the National
Commission for Research, Science and Technology
of Namibia (NCRST) with the approval of the
Ministry of Environment, Forestry and Tourism in
April 2021.
ACKNOWLEDGEMENTS
Our sincere thanks go to the entire Kuzikus team, especially
the Reinhard family, for their inspiring commitment to
rhino conservation and the protection of biodiversity in the
Kalahari. We would also like to thank tracker Gouse
Gammo Divia, Joshua Schlüter, Annika Saunders and
Philipp Sterk for their help in the field, Prof. Dr. Matthias
Waltert and Prof. Dr. Niko Balkenhol from the University
of Göttingen for their support in setting up the research
project and Wild Intelligence Lab for their aerial image
analysis solutions. We gratefully acknowledge Birgit
Kötting (Manager of Namibia's MEFT BRCP) for
providing detailed information on rhino conservation in
Namibia and her continued support of this study, Dr Sky
Alibhai (WildTrack) for assistance with the research
proposal and manuscript, and the reviewers and editors of
the Namibian Journal of Environment for their help and
guidance throughout the publication process.
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