Animal Conservation. Print ISSN 1367-9430
Population recovery of black rhinoceros in north-west
Namibia following poaching
J. F. Brodie1, J. Muntifering2,3, M. Hearn3,4,{, B. Loutit3,{, R. Loutit3, B. Brell3, S. Uri-Khob3,
N. Leader-Williams4 & P. du Preez5
1 David H. Smith Conservation Research Fellow, Wildlife Biology Program, University of Montana, Missoula, MT, USA
2 Minnesota Zoo, Conservation Department, Apple Valley, MN, USA
3 Save the Rhino Trust, Swakopmund, Namibia
4 Durrell Institute of Conservation and Ecology, University of Kent, Canterbury, UK
5 Ministry of Environment and Tourism, Windhoek, Namibia
Keywords
Allee effects; density dependence; hunting;
mark–recapture; matrix model; population
regulation; transient dynamics.
Correspondence
Jedediah F. Brodie, David H. Smith
Conservation Research Fellow, Wildlife
Biology Program, University of Montana,
Missoula, MT 59812, USA. Tel: +1 (406)
274 0505
Email: [email protected]
Editor: Matthew Gompper
Associate Editor: Carmen Bessa-Gomes
{
Deceased.
Received 30 March 2010; accepted 14
December 2010
doi:10.1111/j.1469-1795.2010.00434.x
Abstract
Curtailing overharvest, whether illegal or legal, is often a critical conservation
objective. Yet even if overexploitation can be stopped, subsequent rates of
population recovery can be highly variable due to Allee effects, alterations to age
and sex structure and disruptions of animal social systems. Moreover, understanding the influence of density dependence can be difficult but important for
long-term management. Here, we investigate the dynamics of black rhinoceros
Diceros bicornis in the Kunene region of Namibia as they recover from illegal
hunting. We use multi-strata mark–recapture models to examine survival and
stage-transition rates from 1992 to 2005. Survivorship estimates ranged from 0.793
for calves to 0.910 for adult males and 0.944 for adult females. The annual
reproductive rate in adult females was estimated at 0.315. Model selection showed
that these vital rates were time invariant, suggesting that Allee effects and transient
dynamics did not have an important effect upon population dynamics, even in the
early stages of recovery. Relative population density increased significantly from
1992 to 2005 once illegal hunting had ceased in Kunene. However, the best-fit
models did not include relative density in the estimation of survival or stagetransition rates. We then used the vital rates generated from our mark–recapture
analysis to build matrix projection models that assessed overall population
dynamics. The female-only model gave a population growth rate estimate of
l = 1.011. Two-sex models suggest that the growth rate of the population could
range from 0.990 to 1.012. The relatively slow growth rate of this population, even
without hunting or density dependence, could stem from the low productivity of
the region. Adult females had the highest reproductive value and their survival had
the highest elasticity among vital rates. Translocating adult females would lead to
the fastest initial population growth rate in founder populations but would have
the most impact on the source population.
Introduction
Excessive hunting is one of the most critical threats to large
vertebrates worldwide (Milner-Gulland & Bennett, 2003;
Walsh et al., 2003). Large mammals are being removed at
rates far exceeding sustainable levels in Africa (Fa & Brown,
2009) and the Neotropics (Peres & Palacios, 2007), and are
already lost from most locations in south-east Asia (Corlett,
2007). The rapid destruction of many animal populations
has led to increased effort to reduce the impact of hunting
(Bennett et al., 2007), or in certain situations, to curtail
it altogether (Milner-Gulland, Beddington & LeaderWilliams, 1992).
354
The goal of controlling hunting is to allow the impacted
populations to recover (Stevick et al., 2003). However,
recovery may be far less straightforward that it seems. On
the one hand, we could assume that, in the absence of
human-induced mortality, low-density animal populations
would grow at rates determined by their intrinsic rate of
increase, r (Birch, 1948). The intrinsic rate of increase is
relatively easy to assess indirectly, as it can be estimated
allometrically from life-history parameters such as body size
(Fenchel, 1974; Blueweiss et al., 1978). Nevertheless, populations released from harvest can behave in very unpredictable ways. Following decades of overfishing, the Atlantic
cod Gadus morhua population crashed in 1993 (Bundy &
c 2011 The Authors. Animal Conservation c 2011 The Zoological Society of London
Animal Conservation 14 (2011) 354–362 J. F. Brodie et al.
Fanning, 2005). Yet, despite a subsequent moratorium on
fishing, populations did not recover as expected possibly
because the entire trophic structure of the community had
been altered (Bundy & Fanning, 2005). Even at the population-scale and ignoring impacts on the rest of the food web,
responses to the cessation of harvest can vary greatly for
several reasons. First, hunting can depress the population
size enough to induce Allee effects – reduced population
growth rates at low abundance (Petersen & Levitan, 2001).
Second, hunting can disrupt the age and sex structure of the
population, leading to transient rather than asymptotic
population growth following release from harvest (Koons
et al., 2005; Koons, Rockwell & Grand, 2006). Finally,
hunting can disrupt the social structure of the population
with lasting consequences for long-term population growth.
For example, population recoveries of different species of
ungulates in a protected area in Thailand were highly
dependent on the social behavior of the particular species.
Thus, muntjac Muntiacus muntjak and gaur Bos gaurus
recovered relatively quickly while sambar Rusa unicolor,
whose mating system had been disrupted by hunting that
specifically targeted mature males, remained rare (Steinmetz
et al., 2010). Overcoming Allee effects and the disruption to
the age ratio, sex ratio or social system in the harvested
population can take highly variable periods of time. Thus,
the recovery patterns of formerly hunted animal populations may be very difficult to understand or predict.
Of particular concern in assessing a population’s recovery
from hunting is determining when density dependence
becomes apparent. At this point, population growth may
slow. Certain conservation strategies call for translocations
so as to reduce density dependence in the host population
and promote meta-population structure that lowers the
overall extinction risk (Armstrong & Seddon, 2007; Dimond
& Armstrong, 2007). Yet, understanding how population
density affects vital rates such as survival, growth and
fecundity, as well as changes in abundance, can be difficult
to do. For example, the shape of the relationship between
density and a given vital rate may change across vital rates
or populations, while some vital rates may be more strongly
affected than others (Morris & Doak, 2002). And all of these
need to be estimated from datasets that are often of short
duration, comprise a small sample size or contain significant
sampling error (Ludwig, 1999; Fieberg & Ellner, 2000).
Here, we assess population dynamics of a large ungulate
recovering from intense illegal hunting, the black rhinoceros
Diceros bicornis. Black rhinos were extremely heavily
hunted for their horns and their total population across
Africa was reduced by an estimated 95–97% from 1970 to
1987 (Leader-Williams, 1992; Berger & Cunningham, 1994;
Emslie & Brooks, 1999; Muya & Oguge, 2000). However,
illegal hunting of black rhinos was successfully controlled
across parts of their range by the 1990s (Emslie &
Brooks, 1999; Walpole et al., 2001) and some populations
have grown rapidly since then (Emslie, 2008). The last
known rhino poaching incident in the Kunene region of
north-western Namibia, was in 1994 (B. Brell, pers. obs.).
Very little is known about rhinoceros population dynamics,
Kunene black rhino population recovery
making it difficult to predict the potential impacts of Allee
effects, transient dynamics or disrupted age and sex ratios
on population recovery rates. Our study is one of the first
analyses of a long-term demographic dataset for any species
of rhino and spans 14 years and 259 known individuals.
By following known individuals within the population as it
recovered from 1992 to 2005, we have been able to measure
vital rates across a range of population densities and thus
assess the effects of density on vital rates.
This paper has three objectives: (1) to test for Allee effects
and/or impacts of altered sex and age structure on population growth rates in the early stages of recovery; (2) to
determine whether the population has begun to show signs
of density dependence, and if so, to assess how strong the
effects are and the mechanisms by which they manifest; (3)
to assess individual vital rates and overall population
growth rate in the Kunene black rhino population over the
14-year time period following the control of illegal hunting.
In order to accomplish these objectives, we first use multistrata mark–recapture models to estimate the vital rates of
black rhinos in the Kunene. We then use these vital rates to
build matrix projection models that allow us to characterize
the dynamics of the population and assess the factors
influencing population growth rates.
Methods
Study system and species
The Kunene black rhino population in Namibia is the most
extreme desert-dwelling ecotype of black rhino, and is the
largest free-ranging population of any species of rhinoceros
persisting outside of formal protected areas. Consequently,
the African Rhino Specialist Group has recognized the
Kunene black rhino population as a ‘Key 1’ population
(Emslie & Brooks, 1999). The Kunene population ranges
across c. 20 000 km2 of rugged terrain dominated by basalt
and schist foothills and mountains interspersed with gravel
plains. The area receives c. 150 mm of rainfall per year,
and key resources such as fresh-water springs and vegetation
are distributed unevenly across the landscape (Hearn, 2000).
Land tenure in the region is currently a mixture of
state-administered tourism concessions along with community-managed communal lands. The recent boom in naturebased tourism in Namibia has increased support from
the government and local communities for wildlife conservation, particularly for flagship species such as the black
rhino.
Field surveys
In order to better understand the ecological needs and
population dynamics of Kunene’s black rhinos, a small,
local non-governmental organization, Save the Rhino
Trust, has maintained detailed individual-based sighting
records since the late 1980s. The first well-documented
surveys on the region’s black rhino population were
conducted in 1983 (Loutit, 1983) and again in 1986
c 2011 The Authors. Animal Conservation c 2011 The Zoological Society of London
Animal Conservation 14 (2011) 354–362 355
Kunene black rhino population recovery
J. F. Brodie et al.
(G. Owen-Smith, unpubl. data). A standardized database
containing individual rhino profiles and sighting information was first developed in 1997 (Brett, 1997) incorporating
systematically collected sighting data from 1992 to its status
of over 5000 individual sightings in 2005. Typically, the
rhino tracking operations use teams of three to four trackers
that are vehicle based, although camels are used in roadless
areas. Tracking occurs throughout the year, and surveys are
rotated across eight eco-zones within the range (Hearn,
2000). When fresh tracks are observed either crossing the
road or at fresh-water springs along survey routes, trackers
continue their search by foot. When the rhino is spotted,
vital information such as individual identification, body
condition, location and reproductive condition are recorded, while a photograph is taken of each rhino sighted.
Information from each sighting is entered into the Kunene
black rhino database (Brett, 1997).
Population models
We used these long-term sighting records of individual
rhinos to estimate vital rates using multi-strata, openpopulation models in program MARK 5.1 (Cooch & White,
2001). We collapsed all sightings from a given year to
1 January of that annum, and used a 14-year run of data
from 1992 to 2005. For any given year, sighted individuals
were assigned to one of the following six life-stages: neonate
calf, juvenile male or female (1–4 years old), adult male or
female (5+ years old) or mother-with-neonate. The multi-strata
model partitions the variance in individual capture histories
into separate components representing detectability, apparent survival (i.e. actual survival and fidelity to the study
area) and stage-transition rates. We first tested the goodness-of-fit of the global model to the data using the median-^
c
method (Cooch & White, 2001). We then used model
selection to determine the most parsimonious detectability
function, testing for effects of stage, population density and
time while holding survival and transition functions constant (see Table 1). Using the best-fit detectability model, we
then ascertained the most parsimonious survival function,
and finally the most parsimonious stage-transition function.
We fixed biologically impossible stage transitions to zero
and set the detectability of calves equal to that of motherswith-neonates.
To address objective 1, we tested for Allee effects or
transient dynamics such as those arising from altered sex
and age ratios in the early stages of recovery, by assessing
whether vital rates changed over the 14-year study period.
We did this by explicitly testing time-structured models
of survival and stage transition in the mark–recapture
model set.
To address objective 2, we tested for density dependence
by assessing whether vital rates changed with increasing
relative density using density-structured models in the
mark–recapture model set. We analyzed the abundance of
rhinos over time in the most data-rich portion of the study
area, Zone 6, which provided 31% of the total sightings
over the 14-year study period. We incorporated relative
Table 1 Model selection results from multi-strata, open-population mark-recapture models
Model
Detectability (p) functions
s (stage) p (time) c (stage)
s (stage) p (density) c (stage)
s (stage) p (stage) c (stage)
s (stage) p (stage density) c (stage)
s (stage) p (.) c (stage)
s (stage) p (stage time) c (stage)
Survival (s) functions
s (stage) p (time) c (stage)
s (density) p (time) c (stage)
s (.) p (time) c(stage)
s (stage density) p (time) c (stage)
s (time) p (time) c (stage)
s (stage time) p (time) c (stage)
Stage-transition (c) functions
s (stage) p (time) c (stage)
s (stage) p (time) c (stage density)
s (stage) p (time) c (.)
s (stage) p (time) c(density)
s (stage) p (time) c (stage time)
s (stage) p (time) c (time)
QAICc
QAICc weight
Number of parameters
Quasi-likelihood deviance
2893.8
2896.9
2898.6
2899.8
2903.6
2929.4
0.735
0.155
0.068
0.037
0.005
0.000
22
11
13
17
11
52
2277.6
2303.4
2301.0
2294.0
2310.1
2248.9
2893.8
2897.3
2897.8
2898.9
3240.4
3274.2
0.718
0.129
0.098
0.056
0.000
0.000
22
19
19
27
27
64
2277.6
2287.3
2287.8
2272.3
2613.8
2567.1
2893.8
2901.8
3034.0
3036.0
3254.5
3391.0
0.982
0.018
0.000
0.000
0.000
0.000
22
26
19
20
49
31
2277.6
2277.3
2424.0
2424.0
2580.7
2756.8
Models were first assessed for the most parsimonious detectability (P) function, then survival (s) and finally stage-transitions (c) using corrected
quasi-likelihood Akaike Information Criteria (QAICc). Terms in parentheses indicate the structure imposed on each function for a given model:
‘stage’ refers to life-history stage, ‘density’ to population density (km2) and ‘.’ denotes no structure.
356
c 2011 The Authors. Animal Conservation c 2011 The Zoological Society of London
Animal Conservation 14 (2011) 354–362 J. F. Brodie et al.
Kunene black rhino population recovery
population density as a covariate into the mark–recapture
models, and converted counts of known individuals into
estimates of abundance in year t (N^t ) using Lincoln–Peterson closed-population estimators. We used February and
March, the two consecutive months with the most number
of rhino sightings, as the first and second sampling occasions, respectively. Then
ðMt þ 1ÞðCt þ 1Þ
N^t ¼
1
ðm t þ 1 Þ
JuvF
AdultF
Mother
ð1Þ
Calf
where M and C were the numbers observed on the first and
second occasions, respectively, and m the number observed
on the second occasion that had also been seen on the first.
Detectability in year t (pt) was then estimated as Ct =N^t . We
regressed the estimates of detectability against time and used
the predicted values from this relationship (^
pt ) to estimate
population size from the minimum number of rhinos known
alive each year (see supporting information). Use of these
regression-based detectability estimates accounted for temporal trends in detectability but smoothed out the annual
variance, much of which probably arose from sampling
error.
To address objective 3, we then used the estimates of
survival and stage transitions generated from the multistrata mark–recapture models to build a matrix projection
model for black rhinos, using the techniques described by
Fujiwara & Caswell (2002), which allowed us to assess stagespecific vital rates and overall population growth rate.
Specifically, we built a post-birth census model where the
fertility element was defined as the annual probability
of transition from adult female to mother-with-neonate
(Fig. 1a). Most matrix models of large mammals are
female-only, and this is usually quite reasonable because a
single male could potentially fertilize a large number of
females (Fujiwara & Caswell, 2002; Morris & Doak, 2002).
However, at some point relative male abundance has to
become important. All individuals are limited in their
potential movements, and with large animals such as rhinos
that exist at very low population densities, a single male
could not possibly copulate with every female in the population (Ginsberg & Milner-Gulland, 1994). Therefore, in
addition to the female-only model, we also utilized two-sex
models where birth rates were dependent on the sex ratio
(Fig. 1b). We used the minimum mating function (BessaGomes, Legendre & Clobert, 2010; Legendre, 2004), which
sets an upper limit on the population-wide offspring production equal to the number of adult male–female pairs,
while accounting for polygyny. The number of mating pairs
in year t (ct) is given by
ct ¼ minðNfemales;t ; Nmales;t hÞ
(a)
ð2Þ
where h is the ‘harem size’ or the average number of females
that a reproductively successful male fertilizes (BessaGomes et al., 2010). Genetic evidence from a small black
rhino population in the Save Valley Conservancy, Zimbabwe, suggests that male reproductive success is variable,
and can range from one to three calves per year (Garnier,
JuvM
AdultM
(b)
JuvF
AdultF
Mother
Calf
JuvM
AdultM
Figure 1 Life-cycle diagram illustrating the structure of the black
rhinoceros matrix projection model used here. ‘Juv’ stands for
‘juvenile’, subscripts M and F stand for male and female, respectively.
(a) Standard model where fertility is entirely dependent on females.
(b) Two-sex model where fertility is dependent on the relative
numbers of males and females, linked through a minimum mating
function.
Bruford & Goossens, 2001). Therefore, we tested two
extreme harem size scenarios: h = 1, representing monogamy, and h = 4 representing facultative monogamy (sensu
Clutton-Brock, 1989).
We measured the population growth rate (l) as the
dominant eigenvalue of the projection matrices. We used
Monte Carlo simulations to generate l confidence intervals
(CI) by randomly drawing vital rates from b distributions
determined by the maximum-likelihood estimated mean and
variance for each rate. For each of 10 000 iterations, we
generated projection matrices from the b-random vital rates
c 2011 The Authors. Animal Conservation c 2011 The Zoological Society of London
Animal Conservation 14 (2011) 354–362 357
Kunene black rhino population recovery
The relative density of black rhinos in Kunene region Zone
6, estimated using equation 1 as described above, increased
between 1992 and 2005 (Fig. 2); because of the relationship
between relative density and time, we did not test any
density time interactions in our mark–recapture models.
The variance inflation factor of the global mark–recapture
model, c^ ¼ 1:329, was associated with a significant lack of fit
between model and data (Po0.001). To correct this, we
adjusted the test statistics in future model runs to account
for the extra-binomial variation; therefore we relied on
quasi-likelihood information criteria to compare mark–
recapture models. Initial model runs gave survival estimates
of mothers-with-neonates equal to 1. Because this is biologically unrealistic, in subsequent analyses we fixed the
survival of mothers-with-neonates equal to the survival of
adult females without neonates. Initial model runs also gave
extremely low juvenile ! mother-with-neonate transition
probabilities (1013). In subsequent model runs, we fixed
this transition to zero to afford more accurate estimation of
the remaining vital rates. While calves in our model analysis
are sex independent, the gender of 121 (97%) of the 125
calves was known. The observed calf sex ratio did not differ
from parity (w2 = 1.397, d.f.= 1, P = 0.237), and thus we
fixed the calf ! juvenile transition probabilities to 0.5 for
both males and females.
Holding survival and stage-transitions constant, with
both structured by ‘stage’, we determined that the best-fit
detectability function included ‘time’ as a covariate
(see Table 1). We then used this detectability function to
ascertain that the most parsimonious survival and transition
functions were structured by stage but did not include
relative population density or time. The maximum-likelihood estimates of survival and stage transitions (Fig. 3)
show that estimates of calf survival was the lowest, at 0.793
(95% CI= 0.658–0.884). Furthermore, the only significant
Individuals per 100 km2
8
7
0.90
0.85
0.80
0.75
0.70
0.65
0.60
1.0
Calf
Juv
Juv
Adult
Adult
(b)
0.9
0.8
0.7
0.6
0.5
0.4
0.3
0.2
0.1
0.0
Juv →
Adult
Juv →
Adult
Adult →
Mother
Mother→
Adult
Figure 3 Estimated annual survival (a) and stage-transition rates (b) for
black rhinoceros of different life stages; ‘Juv’ stands for ‘juvenile’,
subscripts M and F stand for male and female, respectively.
difference, comprising non-overlapping 95% CI, was between adult female survival (0.944; 95% CI= 0.920–0.962)
and calf survival. The transition rate from adult female to
mother-with-neonate was 0.315 (95% CI= 0.265–0.369),
and this also served as the annual fertility estimate for adult
females. The probability that mothers-with-neonates returned to the adult female stage in the following year was
very high, at 0.985 (95% CI= 0.901–0.998).
We estimated the population growth rate for black rhinos
in the Kunene region in three ways: as l = 1.011 (95%
CI = 0.991–1.030) using the female-only model; as
l = 0.990 (95% CI = 0.967–1.013) using the two-sex model
where h = 1; as l = 1.012 (95% CI= 0.993–1.031) using the
two-sex model where h = 4 (Fig. 4). Survivorship of adult
females showed the highest elasticity, at 0.239 (Fig. 5).
6
5
Discussion
4
3
2
1
19
92
19
93
19
94
19
95
19
96
19
97
19
98
19
99
20
00
20
01
20
02
20
03
20
04
20
05
0
Year
Figure 2 Relative black rhino density in Zone 6 of the Kunene region.
358
(a)
0.95
Estimated
annual survivorship with
95% confidence intervals
Results
1.00
Estimated
annual stage transition probabilities
with 95% confidence intervals
and calculated l for each. We then sorted the l arrays and
used the 250th and 9750th values as 95% confidence limits
(Caswell, 2001).
J. F. Brodie et al.
Population recovery from harvest can be very unpredictable
because overhunting can lead to Allee effects (e.g. Petersen
& Levitan, 2001); alter the age and sex structure of the
population leading to transient growth (Koons et al., 2006);
or disrupt animal social systems (e.g. Steinmetz et al., 2010).
We investigated population dynamics of a desert-dwelling
black rhino population over 14 years following the successful control of poaching in the Kunene region of north-west
Namibia. Our mark–recapture analysis showed no support
c 2011 The Authors. Animal Conservation c 2011 The Zoological Society of London
Animal Conservation 14 (2011) 354–362 J. F. Brodie et al.
Kunene black rhino population recovery
0.35
1.03
0.30
Stable stage distribution
1.04
1.02
1.01
1.00
0.99
0.98
0.97
0.96
Female-only
model
Two-sex model
(h = 1)
Two-sex model
(h = 4)
0.20
0.15
0.10
0.05
4.0
Calf
JuvF
AdultF Mother
JuvM
AdultM
JuvF
AdultF Mother JuvM
AdultM
JuvF
AdultF JuvF→ Calf→ AdultF→
AdultF JuvF Mother
(b)
Reproductive value
3.5
3.0
2.5
2.0
1.5
1.0
0.5
0.0
0.26
Calf
(c)
0.24
0.22
Elasticity
for models where survival or stage transitions varied over
time. This suggests that neither Allee effects nor transient
dynamics reduced survivorship or reproduction to any
detectable degree in this population early in its recovery. In
contrast, Allee effects were reported for black rhinos in
Pilanesberg, South Africa (Hrabar & du Toit, 2005), where
maternal success increased with density up to
0.085 rhinos km2. In Pilanesberg, female age structure and
adult sex ratio changed during the early stages of population
growth, which could have resulted in the observed Allee
effects (Hrabar & du Toit, 2005), although it was not
reported whether trends in maternal success translated into
impacts on overall population dynamics. The reason that
Allee effects were not observed in the Kunene could be due
to smaller changes in age and sex structure during the early
stages of population growth, or else that those changes in
age and sex structure did not detectably manifest at the
population level.
Interestingly, despite the increasing relative density of
Kunene rhinos over 14 years (Fig. 2), we did not detect any
important density-dependent effects in the Kunene black
rhinos. It may be that density has not yet reached levels at
which it negatively affects vital rates across the whole
population, although smaller areas within the current range
might still be experiencing density-dependent effects that
were not detectable at the scale of our analysis. The
continued monitoring of the Kunene rhino population
might provide important information on density dependence. Our population growth rate estimates and temporal
trends in relative density both suggest that population
density will continue to increase over the near term, but at
a much lower rate than the intrinsic rates of increase known
to be possible for rhinos, which generally range from 4 to
11% (Okita-Ouma et al., 2009). If density does continue to
rise in the Kunene, density dependence may begin to affect
particular vital rates at some point. Determining exactly
how density dependence affects population dynamics will be
critical for managers (Morris & Doak, 2002). For example,
an understanding of density dependence is necessary to
accurately estimate long-term population size, environmental
0.25
0.00
Figure 4 Estimated Kunene black rhino population growth rate (with
95% confidence intervals) based on different matrix model structures.
(a)
0.20
0.18
0.16
0.14
0.12
0.10
Calf
Survival rates
Stage transitions
Figure 5 Proportion of individuals in each stage at stable distribution
(a), reproductive value of different stages (b) and analytical elasticities
across female vital rates (c). ‘Juv’ stands for ‘juvenile’, subscripts M
and F stand for male and female, respectively.
carrying capacity and population trends. Moreover, translocation strategies can be made particularly effective if
animal removals are conducted in a way that reduces density
dependence in the donor population (Todd, Nicol & Koehn,
2004).
Another productive line of future research would involve
a further examination of the role of males in rhino population dynamics. Males are clearly important at some level
(Ginsberg & Milner-Gulland, 1994), but our currently
limited data on male reproductive success (Garnier et al.,
c 2011 The Authors. Animal Conservation c 2011 The Zoological Society of London
Animal Conservation 14 (2011) 354–362 359
Kunene black rhino population recovery
2001) afford only imprecise estimates of their importance to
population dynamics. For example, the two harem size
scenarios that we examined resulted in quite different
estimates of population growth rate, from ‘likely declining’
at h = 1 to ‘likely increasing’ at h = 4. An important consideration, although not one that has received much attention in the literature (see Caswell, 2001; Sundelöf & Åberg,
2006), is that h could very plausibly vary with male density.
As the ratio of males to females declines in the population,
we might expect the reproductive success rate of a given
individual male to increase. However, given the paucity of
data that allow us to estimate even static measures of h, it
will be very difficult indeed to assess the factors influencing
variation in h.
Our female-only matrix models suggest that the Kunene
black rhino population grew at 1% per year from 1992 to
2005. This relatively slow rate of growth could be attributable to the extreme aridity and low productivity of the
region (Hearn, 2000). Our population growth rate estimates
may be useful for understanding the dynamics of this
population as it continues to recover from intense poaching.
Moreover, our models could prove useful for informing
management decisions regarding translocations of individuals and/or trophy hunting. The Rhino Custodianship
Program of the Namibia Ministry for Environment and
Tourism (MET, 2003) currently manages the country-wide
population of D. b. bicornis in accordance with continentalscale recovery goals (Emslie et al., 2009). The main objective
of Namibia’s National Rhino Policy (MET, 2003) is to
maximize growth rates through strategic removals in highdensity areas while increasing the range and population size
through translocations within the historical range. New
population centers are being established because ‘custodians’, including Namibians on both private and communal
lands, now have the opportunity to benefit economically
from having black rhino back on their lands in exchange for
providing protection and monitoring. Long-term management goals are to re-establish a connected population of at
least 2000 black rhino in suitable habitat across Namibia,
develop both consumptive and non-consumptive sustainable use policies and to achieve better overall co-ordination
for protection and management of rhino in Namibia. Such
ambitious goals may benefit tremendously from long-term
black rhino monitoring data. In the north-west Kunene
region, where annual black rhino translocations into communal lands are ongoing, Save the Rhino Trust continues to
manage the largest and longest-running database on any
black rhino population in Africa. This source of long-term
rhino demography and movement data has the potential to
be a very useful tool for biological management. We know,
for example, that adult females have the highest reproductive value and that survivorship of these individuals has the
highest elasticity of any vital rate. These findings, consistent
with those for other long-lived animals (Pfister, 1998),
suggest that translocating adult females would ensure the
highest initial population growth rate in the founder communities but would have the most impact on the growth rate
of the source population.
360
J. F. Brodie et al.
Acknowledgments
We offer sincere thanks to the many trackers and staff of
Save the Rhino Trust, Namibia. Funding for this project
was provided by many private donors, the UK’s Darwin
Initiative for the Survival of Species, US Fish and Wildlife
Service, United Nations Development Program, David
Shepherd Wildlife Foundation, Save the Rhino International and Round River Conservation Studies. J.F.B. was
funded during analysis and writing by a David H. Smith
Conservation Fellowship from the Society for Conservation
Biology and the Cedar Tree Foundation. We are grateful to
J. Berger, K. Griffin, L.S. Mills, E. Crone and two anonymous reviewers for suggestions with the analysis and
comments on previous versions of the paper.
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Supporting Information
Additional Supporting Information may be found in the
online version of this article:
Figure S1. Estimates of detectability over time
(^
y ¼ 0:009x 16:720; R2 = 0.131).
362
J. F. Brodie et al.
Figure S2. Trends in relative density estimates from
three different estimation methods: Lincoln-Peterson markrecapture, minimum number known alive (MKNA), and
MKNA corrected by detectability. MKNA/detectability
values are reported in the main text, figure 2
As a service to our authors and readers, this journal provides
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issues arising from supporting information (other than missing files) should be addressed to the authors.
c 2011 The Authors. Animal Conservation c 2011 The Zoological Society of London
Animal Conservation 14 (2011) 354–362 C 2005 The Zoological Society of London. Printed in the United Kingdom
Animal Conservation (2005) 8, 259–267 doi:10.1017/S1367943005002234
Dynamics of a protected black rhino (Diceros bicornis)
population: Pilanesberg National Park, South Africa
Halszka Hrabar† and Johan T. du Toit
Mammal Research Institute, Department of Zoology and Entomology, University of Pretoria, Pretoria, 0002, South Africa
(First received 20 August 2003; resubmitted 24 April 2004; accepted 2 December 2004)
Abstract
Achieving maximum productivity in remnant populations of black rhinoceros is crucial to the persistence of
this species. It was, therefore, investigated whether the black rhino population of Pilanesberg National Park
had become regulated by resource limitation 22 years after introduction in 1979. Inter-calving intervals (which
are not restricted to yearly time increments, due to asynchronous reproduction) decreased with an increase
in rainfall, while the percentage of male calves born increased with increasing rainfall. The percentage of
reproductive cows achieving maternal success increased with increasing density until 0.085 rhinos/km2 , after
which it decreased. This positive relationship at low densities is largely due to changes in the female age
structure and the adult female/male ratio. The age at first calving tended to increase with increasing density,
while mortality was not related to rainfall or density. It is concluded that the Pilanesberg black rhino population
is showing the first signs of density dependence. It is proposed that black rhino conservators should monitor
the percentage of cows achieving maternal success to detect early indications of density dependent resource
limitation and use this as a criteria for decisions regarding metapopulation management.
INTRODUCTION
In just 18 years, from 1970 to 1987, black rhino (Diceros
bicornis) numbers declined by 95% across the species’
range (from 65 000 to 3800 animals: Muya & Oguge,
2000). Today, the species population not only remains at
a critically low level, but is also fragmented into smaller,
isolated populations. As a result, problems associated with
small populations, such as environmental, demographic
and genetic stochasticity, are quite probable (Woodroffe &
Ginsberg, 1998). While subpopulations should ideally be
large enough to prevent such problems from occurring
in the long term (i.e. > 200 years), the small size of
most protected areas makes this impossible for black
rhinos (Foose et al., 1993). Consequently, metapopulation
management has been recommended for black rhino
conservation, whereby the judicious translocation of
animals between subpopulations should be maintained
to prevent adverse genetic and demographic conditions
(Foose et al., 1993). In addition, achieving maximum
productivity in all populations is crucial for decreasing
their vulnerability to extinction. An understanding of
black rhino demography and the factors affecting their
productivity is therefore essential.
†
All correspondence to: H. Hrabar: Tel: +27 12 4204609; Fax: +27
12 4202534; E-mail: [email protected]
A central concern in population biology is the importance of density dependence in regulating population
size (Hanski, 1990; Saether, 1997; Lundberg et al.,
2000). The results of several long-term individual-based
population studies of ungulates have shown that in a
predator-free environment, the population dynamics of
ungulates are strongly influenced by a combination of
stochastic variation in the environment and population
density (Clutton-Brock et al., 1987, 1992; Owen-Smith,
1990). Resource availability per individual is reduced at
high population densities, affecting mortality, fecundity
(calves born per adult female per year) and age at maturity
(Saether, 1997). Both adult and calf mortality have been
found to increase with the onset of resource limitation
(Sinclair, Dublin & Borner, 1985; Clutton-Brock et al.,
1987, 1997; Mduma, Sinclair & Hilborn, 1999), while
fecundity decreases due to increasing age at maturity
(Gaillard et al., 1992; Festa-Bianchet, Urquhart & Smith,
1994; Langvatn et al., 1996) and increasing inter-calving
interval (Fowler, 1981; Owen-Smith, 1990; Clutton-Brock
et al., 1991; Forchhammer et al., 1998). Such effects
of density dependence could have a significant effect on
black rhino population performance, so the challenge is
to maintain maximum reproductive performance while
keeping subpopulations large enough to avoid stochastic
demographic problems.
To minimise density-dependent effects in southern
African subpopulations, the Rhino Management Group
(RMG) recommends that populations be managed at 75%
260
H. HRABAR AND J. T. DU TOIT
of ecological carrying capacity, K, with surplus animals
being used for introductions elsewhere (Adcock, 2000).
The difficulty, however, is in determining what K is, within
the extent to which K is a useful concept in savanna
ecosystems (see Illius & O’Connor, 1999). One method is
to use the comparative approach, where population data
from a number of areas of similar rainfall and habitat are
used to derive a conservative estimate of K for the area in
question (Bell, 1986). The RMG carrying capacity model
is an example of such an approach, since 15 areas were
compared across six variables (annual rainfall, rainfall
concentration, minimum July temperature, soil fertility,
browse availability and browse suitability: Adcock, 2001).
A second approach, however, is to monitor a focal population over time to detect from population parameters
when density dependent resource limitation comes into
affect and then to apply adaptive management (Walters &
Hilborn, 1984; Saether, Engen & Lande, 1996). This is
a more effective approach (where and when possible)
because K is not a set value, but rather a broad approximation that varies as conditions change (Macnab, 1985),
for example as demonstrated in southern Africa where
interannual rainfall variation is known to have a significant
influence on ungulate population dynamics (Novellie,
1986; Owen-Smith, 1990).
A black rhino population that provides the opportunity
to study the effect of increasing density and variable rainfall on population performance, is that of the Pilanesberg
National Park (Pilanesberg). The population has not only
been well monitored for nearly 20 years, but has also
been steadily increasing in size during this period and has
now reached its original estimated K of 0.1 rhinos/km2
(Adcock, 2001). The aim of this study was, therefore, to
determine whether, in the absence of hunting, the Pilansberg black rhino population is showing signs of being at or
near K and, if so, if density-dependent or density-independent factors are limiting population growth. The study set
out to specifically investigate whether indicators of population performance (age at maturity, inter-calving interval,
percentage of females achieving maternal success and
mortality) changed predictably in response to increasing
population density and rainfall.
Furthermore, we investigated whether variation in sex
ratio at birth could be explained in this black rhino population by environmental factors that influence maternal
condition. According to Trivers & Willard (1973), in
species where one sex has a more variable reproductive
success (such as males in polygynous species), natural
selection will favour the ability of females to vary the sex
ratio of their offspring in relation to their body condition.
The degree of territoriality in black rhinos is debatable
(Goddard, 1967; Frame, 1980; Kiwia, 1989) but territorial
dominance is known to exist, suggesting that (Adcock,
Hansen & Linderman, 1998) reproductive success between males is variable. We should thus expect black
rhino females in poor condition at conception and during
gestation to tend towards producing daughters, but when
in good condition they should tend towards producing
sons, assuming an offspring’s future reproductive success
is related to its size and/or condition at birth.
MATERIALS AND METHODS
Study area
Pilanesberg National Park is located in the North West
Province of South Africa, between 25◦ 22 S and 25◦ 70 S
and between 26◦ 56 E and 27◦ 14 E. It covers 476.41 km2
of rocky hills and broad alluvial valleys in a weathered
alkaline volcano (Adcock et al., 1998). The habitat
consists of Acacia and broad-leaf bushveld, ranging from
open grassland to thicket. The mean annual rainfall is
630 mm, falling predominantly in summer. Winters are
cold (average minimum 2.5 ◦ C, average maximum 22 ◦ C)
and summers hot (average minimum 17.5 ◦ C, average
maximum 31 ◦ C: Bailey, Brockett & Mentis, 1993).
After the Park was proclaimed in 1979, 19 black rhinos
were introduced between 1981–1983, and a further five
individuals in 1989. The population increased steadily,
allowing nine individuals to be translocated to Madikwe
Game Reserve in 1996. By December 2001 the total
population had reached 55 (Fig. 1).
Data collection
From 1981 to 1983 the population was monitored by
Hillman (1982, 1983, 1984), but from 1984 to 1989 only
annual helicopter counts were carried out. Older individuals were identifiable by recognisable characteristics such
as ear tears and horn shape. Consequently, this enabled individual survival and female reproductive success (if they
had a calf at foot or not) to be recorded during this period.
From 1989 monitoring efforts were increased and in
1991 an ear-notching program was started to enable the
development of a rhino identification system, which has
been updated almost annually. All black rhinos in the Park,
except infant calves, are therefore individually recognisable. Reliable estimates of vital rates (survival, fecundity
etc) of this wild population were therefore possible, as
marked individuals could be accurately monitored in the
long term (Gaillard, Festa-Bianchet & Yoccoz, 1998).
Since 1989, ground monitoring was based on a game
scout-training program, where game scouts were trained
to collect information such as individual identification,
sighting location and date. In addition, a full time black
rhino monitoring officer has ensured the more intensive
monitoring of each individual in the Park (i.e. seen at
least once a year). Birth dates were determined by aging
calves according to their body size and length of horn.
Conception dates were calculated as being 15 months
(Adcock, 2000) prior to the date of birth.
Population performance
All introduced rhinos were adults or sub-adults, resulting
in the population age structure having an adult bias during
the initial years following the introductions. In more recent
years, however, the age structure has been skewed towards
juveniles and sub-adults, as many calves have been born in
the Park, but have not yet reached adulthood (Fig. 1). The
population growth rate was therefore not used to assess
population performance, since, even if all reproductive
Black rhino population dynamics
60
(a)
Adults
50
Sub-adults
Juveniles
40
30
20
10
0
(b)
12
Juveniles
10
8
6
Number of individuals
4
2
0
12
(c)
Sub-adults
10
8
6
4
2
0
20
(d)
Adults
16
12
8
4
01
99
97
95
93
91
89
87
85
0
Year
Fig. 1 (a) The number of black rhinos in Pilanesberg National Park
from 1985–2001 (note that in 1996 nine individuals were removed
from the Park). (b)–(d) The change in the number of juvenile, subadult and adult black rhinos in Pilanesberg National Park over time.
Solid bars, females; open bars, males. Age classes as noted in the
Methods.
females had a high reproductive success, their low
numbers in the population would give a misleadingly low
population growth rate (Saether, 1997). We therefore used
inter-calving intervals (ICI), age at maturity (first calving),
261
mortality and the percentage of adult females achieving
maternal success (raising a yearling) as performance
indices.
For each calving event (except a female’s first calf), we
calculated the inter-calving interval as the number of years
since the birth of the previous calf and assigned this value
to the year in which conception took place. It is important
to note that reproduction in black rhinos is asynchronous,
with calves being born (and conceived) throughout the
year. Inter-calving intervals are therefore not restricted to
yearly time increments.
We calculated the percentage of adult females achieving
maternal success for all females aged ≥ 6 years (the
youngest age of first calving) and again for only those
females that had started calving. This was to see whether
the increasing number of young adult females (aged
6–8 years) in the population affected the percentage
reproducing, as the probability of calving may be lower
for this age group (Langvatn et al., 1996; Adcock et al.,
1998). We determined mortality rates, calculated as the
percentage of individuals dying per year, for juveniles
(< 3 years old), sub-adults (males 3–7 years, females 3–
5 years) and adults (males ≥ 7 years, females ≥ 5 years).
We tested each index of population performance for
associations with the population density and/or rainfall
(see below). Density and rainfall were considered as
independent variables, as they were not significantly
correlated (F1,13 = 3.705, P < 0.076, R2 adj = 0.162). Their
weak relationship was most probably due to the occurrence
of male deaths from fighting during low female/male ratio
years, which happened to coincide with a period of low
rainfall. Age at maturity was compared to the density and
rainfall during the year of first conception and the 2 years
prior to the conception year (with each year considered
as a separate variable), as an individuals’ growth and
development is influenced by resource availability over a
number of years. Inter-calving intervals, the percentage
of females achieving maternal success and mortality
rates were compared to the corresponding density and
rainfall (July–June) values for each year. Although shown
graphically, the ICI value for 1986 (4.5 years) was not
included in the analyses, as it is based on one individual
and the validity of the long ICI is questionable due to the
less intensive monitoring efforts at that time.
The monthly distribution of conceptions (averaged
across all years) in relation to rainfall was also tested.
We compared the number of conceptions per month to the
mean rainfall for that month. For example, the number of
conceptions that took place during January was compared
to the mean rainfall for January.
We used multiple linear regression analysis to test
for significance in relationships (Rosner, 1995; Zar,
1996) and statistical analyses were conducted using
STATISTICA (Statsoft, 1991). All percentage data were
arcsine transformed.
Calf sex
A direct comparison between female body condition at
the time of conception and calf sex was not possible, since
262
H. HRABAR AND J. T. DU TOIT
Table 1. Results of four multiple regressions, each testing how a measure of population performance is affected by rainfall and density
Independent
variables
Regression coefficient (β)
Standardised
coefficient (b)
P-value
Inter-calving interval
Rain
Density
− 0.0014 ± 0.005
− 9.5747 ± 7.005
− 0.5787
− 0.2976
0.0223
0.1990
Maternal success (adult females)
Rain
Density
0.0206 ± 0.0143
606.8092 ± 207.0157
0.3049
0.6222
0.1814
0.0150
Juvenile mortality
Rain
Density
4.3757 ± 2.3208
− 0.0001 ± 0.0002
0.5394
− 0.1584
0.0838
0.5900
Adult mortality
Rain
Density
0.0000 ± 0.0001
− 2.8250 ± 1.4391
0.0747
− 0.5493
0.7941
0.0732
Dependant variable
R2 adjusted and P-value for each multiple regression model:
Inter-calving interval – R2 adj = 0.477, P = 0.011.
Maternal success – R2 adj = 0.498, P = 0.013.
Juvenile mortality – R2 adj = 0.107, P = 0.201.
Adult mortality – R2 adj = 0.147, P = 0.153.
body condition data were not available. Relationships
between body condition and extrinsic factors such as
population density and climate have, however, been well
documented (Kruuk et al., 1999; Post et al., 1999;
Mysterud et al., 2000). Therefore, assuming that female
black rhino body condition is influenced by rainfall and
the mortality of male and female newborns is equal,
we calculated the number of male and female calves
conceived during years with less than, and more than,
640 mm of rainfall (the mean annual rainfall over the study
period). A chi-squared goodness-of-fit test, incorporating
Yates correction factor (Everitt, 1977), was used to
determine whether calf sex ratio differed significantly
from the expected 50:50 ratio during years of high or
low rainfall.
In addition, the sex of calves born to primiparous
females, the tendency for individual females to produce
one sex and trends in the sex of sequential calves born to
individual females was investigated.
RESULTS
Population performance
The age at which females first calved ranged from 6–
8.92 years (x̄ = 7.25, standard error (SE) = 0.22, n = 12)
with 33% of females having calved before they were
7 years old, 83% before 8 years and 100% before 9 years.
The age at first calving was not found to be related to
the combined effect of density and rainfall (F6,5 = 1.251,
P < 0.412, R2 adj = 0.120), nor rainfall alone (F3,8 = 2.077,
P < 0.182, R2 adj = 0.227). Even though the relationship
was not significant, there was a definite tendency for the
age at first calving to increase with increasing density
(F3,8 = 3.461, P< 0.071, R2 adj = 0.402).
Inter-calving intervals ranged between 1.67 years and
5.17 years (x̄= 2.83, SE = 0.11, n = 47). The negative
relationship between ICIs and density was nearly
significant (F1,12 = 4.508, P = 0.055, R2 adj = 0.213), yet
a multiple regression including both rainfall and density
showed rainfall to be the most important variable affecting
ICIs (Table 1). When density was dropped from the model,
the significance of the relationship with rainfall increased.
ICIs were therefore best explained by rainfall alone,
with intervals decreasing with increasing rainfall (Fig. 2).
The timing of conceptions in the year was also related
to rainfall, since the monthly number of conceptions
increased significantly with an increase in monthly rainfall
(Fig. 3).
No significant linear relationship was found between
the percentage of females achieving maternal success and
rainfall and/or density. When only data for densities of up
to 0.085 rhinos/km2 (the percentage of females achieving
maternal success was highest below this density) were
included in the analysis, however, the percentage of
females achieving maternal success was significantly
positively related to density (Fig. 4) and this relationship
was strongest when rainfall was not included in the
model (see multiple regression results, Table 1). An
increase in density therefore resulted in an increase in
the percentage of females achieving maternal success
until a density of 0.085 animals/km2 , after which there
began a decline with increasing density (Fig. 4). When
only reproductive females were considered, i.e. excluding
those females of 6 years and older that had not yet
calved, the relationship with density was barely significant
(F1,11 = 4.937, P = 0.048, R2 adj = 0.247), indicating that
the positive relationship with density was largely due
to changes in the female age structure. There was,
however, still a marked decline in reproductive success
above a density of 0.085 animals/km2 . All juvenile deaths
during this time were from mothers of 14 years and older
and were, therefore, not due to an increase in young,
less experienced, cows. Density, rather than female age
structure, therefore best explains this decline in maternal
success in recent years.
Black rhino population dynamics
(a)
(b)
5.0
950
850
3.8
750
3.2
650
2.6
550
2.0
Inter-calving interval (years)
ICI
RAIN
4.4
1.4
5.0
1050
Rainfall (mm)
Inter-calving interval (years)
263
450
1986 1988 1990 1992 1994 1996 1998 2000
Year
350
y = -0.0017x + 3.858
s = 0.291
R2 = 0.439
4.4
3.8
3.2
2.6
2.0
1.4
350
450
550
650
750
Rainfall (mm)
850
950
1050
Fig. 2 (a) Graph of the annual mean inter-calving intervals (ICIs) and the mean annual rainfall (July–June). (b) Regression of the mean
ICIs against mean annual rainfall (F1,12 = 11.173, P = 0.005, R2 adj = 0.439), excluding data from 1986 (circled).
(a)
(b)
14
120
12
10
100
10
8
80
6
60
4
40
2
20
0
0
Conceptions
Rain
Number of conceptions
12
-2
J
F M A M J J A S O N D
Month
-20
Number of calves
140
Rainfall (mm)
14
y = 0.0506x + 2.678
s = 2.927
R2 = 0.330
8
6
4
2
0
-2
-20
0
20
40
60
80
Rainfall (mm)
100
120
140
Females achieving maternal success (%)
Fig. 3 (a) Graph of the monthly distribution of conceptions and the mean monthly rainfall over the duration of the study period.
(b) Regression of the number of calves born per month against mean monthly rainfall (F1,10 = 6.425, P = 0.030, R2 adj = 0.330).
46
y = 686.3 x -14.483
40
s = 7.551
R2 = 0.449
34
28
22
16
10
0.045 0.050 0.055 0.060 0.065 0.070 0.075 0.080 0.085 0.090
Density (rhinos/km2)
Fig. 4 The relationship between the percentage of females achieving
maternal success and population density. Note that the regression
excludes those points from the last 2 years (circled) when the
density was ≥ 0.085 rhinos/km2 (F1,11 = 10.793, P = 0.007, R2 adj =
0.449).
To test whether the effect of density on maternal
success (at low densities) was mainly due to a ‘year’
effect, ‘year’ was also treated as an independent variable.
The percentage of adult females achieving maternal
success was positively related to year (F1,11 = 9.748,
P = 0.010, R2 adj = 0.422), while the relationship for the
percentage of reproductive females was almost significant
(F1,11 = 4.739, P = 0.052, R2 adj = 0.238). This is to be
expected when a small founder population is introduced
into a closed system (as with the black rhinos in
Pilanesberg), with population density increasing steadily
over time. However, the decrease in significance of the
relationship when only reproductive adult females were
considered, together with the decline in maternal success
in recent (high-density) years, indicates that maternal
success is truly influenced by those variables changing
over time (such as density and female age structure), rather
than ‘year’ itself.
Twenty-two black rhino deaths were recorded from
1985 to 2001 (nine juveniles, zero sub-adults and 13
adults). For adults, the most common known cause of
death was either fighting between males (n = 3) or old
age (n = 3). The cause of all juvenile deaths and the
remainder of the adult deaths are, however, unknown.
Juvenile mortalities were highest during low rainfall years
and recent high-density years (Fig. 5), yet neither juvenile
nor adult mortality were significantly related to rainfall
and/or density (Table 1).
264
H. HRABAR AND J. T. DU TOIT
1050
40
Deaths
Rain
950
30
850
25
20
750
15
650
10
550
Rainfall (mm)
Percentage of juveniles dying
35
5
450
0
-5
350
1986
1988
1990
1992
1994
Year
1996
1998
2000
Fig. 5 Graph showing the percentage of juveniles dying each year
compared to the annual rainfall (July–June).
Calf sex
Over the duration of the study period the calf sex ratio
was exactly equal, with 27 males and 27 females being
born. This ratio was not constant over time, however, as
there were significantly more females conceived during
years with below average rainfall (χ2 = 3.885, df = 1,
P < 0.05) and significantly more males conceived during
years with above average rainfall (χ2 = 4.316, df = 1,
P < 0.05). It therefore appears that female body condition
may influence calf sex in black rhinos.
Of the nine known-sex calves born to primiparous
females, five were female and four were male. All, except
for one out of 13 reproductive females, produced both
male and female calves. The two females with the largest
sample size of calves (six) both produced an equal number
of males and females. There is, therefore, no tendency
for primiparous females to produce a certain sex calf,
or for individual females to produce one sex. However,
for calves born to individual females, the occurrence of
successive calves alternating in sex was almost double that
of successive same-sex calves (17 versus 9 occurrences,
respectively). There does, therefore, seem to be a trend for
black rhinos to alternate the sex of their calves.
DISCUSSION
Population performance of the Pilanesberg black rhino
population was found to be influenced by both densitydependent and independent factors. The percentage of
females achieving maternal success was influenced by
density, while inter-calving intervals were influenced by
rainfall.
The increase in inter-calving intervals with decreasing
rainfall is in accordance with the literature, as the links
between rainfall and resource availability, and nutritional
status and reproductive capacity, have been well documented. In African savanna ecosystems, for example,
vegetation production has been found to be linearly related
to rainfall where the total annual rainfall is below about
800 mm (Coe, Cumming & Phillipson, 1976; Rutherford,
1980). In addition, resource availability fluctuates over
time with the seasonality of rainfall (Rutherford, 1984;
Ostfeld & Keesing, 2000). Individual growth and body
condition depend on resource availability, which therefore
influences reproduction. Age at first calving, for example,
increases under conditions of resource limitation because
the onset of puberty is dependent on body mass (Joubert,
1963; Hamilton & Blaxter, 1980). Inter-calving intervals
are also affected, since evidence shows that under-weight
females, or females on nutrient-poor diets, may fail to
ovulate (Lindsay, 1976; Thomas, 1982). Periods of low
rainfall must reduce the body condition of female black
rhinos by reducing the quantity and quality of their food
supply, that being forbs and woody browse (Goddard,
1970; Mukinya, 1977; Hall-Martin, Erasmus & Botha,
1982; Oloo, Brett & Young, 1994; Muya & Oguge, 2000),
as confirmed by the positive association between the
seasonal rainfall pattern and the occurrence of conceptions
in the Pilanesberg population.
Population density would also influence resource
availability, since the level of consumption and, hence,
the period over which dietary components remain into the
dry season, would depend on population density when the
population size approaches K (Owen-Smith, 1990). Reproductive performance is therefore also density dependent (Albon, Mitchell & Stains, 1983). In the Hluhluwe–
Umfolozi Game Reserve, for example, the age at first calving in black rhinos was found to increase with an increase
in population density, since the mean age at first calving
was 6.5 years and 12 years in areas of low (0.1 animals/
km2 ) and high (0.7 animals/km2 ) density, respectively
(Hitchins & Anderson, 1983). In Pilanesberg, there was a
tendency for age at first calving to increase with increasing
density, yet even when the density was at its highest
(0.12 animals/ km2 ), females first calved between 6.83
and 7.13 years, which is comparable to the low-density
Umfolozi population. It should be noted, however, that
the high rainfall received during high-density years could
have reduced the effects of density in Pilanesberg.
Inter-calving intervals are also known to be influenced
by density, with high densities resulting in an increase
in the time between calves (elephant, Laws & Parker,
1968; white rhino, Rachlow & Berger, 1998). The trend in
Pilanesberg was, however, opposite to this. Similarly, the
percentage of adult females achieving maternal success
(which is partly dependant upon ICIs) was positively
related to density at low densities. This relationship did
weaken when only reproductive females were considered,
yet it was still significant and therefore could not be
explained by female age structure alone. One other
explanation for this unexpected pattern may be the Allee
effect (Stephens & Sutherland, 1999). If densities are
too low, for example, interactions between males and
females in oestrus may be more infrequent, thereby
decreasing mating opportunities. This has, however, not
been reported for any other small rhino populations
(Adcock et al., 1998). Alternatively, the explanation
could be that of social behaviour, since reproductive
performance in black rhinos is known to increase with
an increase in the female/male ratio (Adcock et al., 1998).
In Pilanesberg, the female/male ratio was particularly low
Black rhino population dynamics
(≤ 1 female/male until 1991) in early, low-density years
(Fig. 1), making this a probable explanation. The pattern
of increasing reproductive performance with increasing
density may, therefore, not be a typical response for black
rhinos, but is rather due to the ‘more favourable’ adult sex
ratio at higher densities in this case.
The decrease in calving success at densities
≥0.085 rhinos/km2 suggests that density-dependent resource limitation may have begun to affect the population
at this stage (Fig. 4), as rainfall was still above average and
the female/male ratio had not decreased. Interestingly, no
such trend was apparent in the ICIs. Inter-calving intervals
are, however, only derived from the cows that calve, but
the proportion of females reproducing successfully (which
takes into account neonatal mortality) provides a more
direct indication of how female reproductive success is influenced by environmental variation. In addition, juvenile
deaths are reflected in ‘maternal success’ and, in this case,
are largely responsible for the decline observed in recent
high-density years.
The degree to which rainfall affects reproduction in
black rhinos was also reflected in the effect on calf
sex. As with numerous other ungulate studies (Saltz &
Rubenstein, 1995; Wauters et al., 1995; Cameron et al.,
1999; Kruuk et al., 1999; Mysterud et al. 2000; Côté &
Festa-Bianchet, 2001), the proportion of male calves
born was positively associated with rainfall prior to and
during the time of conception and, therefore, to food
abundance and, presumably, body condition, as predicted
by the Trivers–Willard Hypothesis. While such departures
from expected calf sex ratio variations could potentially
have applications for conservation monitoring, evidence
supporting the Trivers–Willard hypothesis is mixed (Saltz,
2001; Carranza, 2002) and no mechanisms by which the
hypothesis works have yet been proved in mammals.
In Pilanesberg, an index of black rhino population
performance that was not influenced by rainfall or density,
was mortality of sub-adults and adults. No sub-adult
and only three adult mortalities (two due to old age)
were recorded since 1994, in higher density years. This
is not unexpected, first because the population density
has been comparatively low until recently. Second, largebodied mammals such as black rhinos generally exhibit
a K-selected life-history strategy whereby lifetime reproductive success is dependent on a long life-span,
during which starvation may be offset in times of resource
limitation by temporarily forfeiting reproduction (Begon,
Harper & Townsend, 1996; Coulson, Milner-Gulland &
Clutton-Brock, 2000).
According to Eberhardt (2002), changes in vital rates
take place as a specific sequence of events in response
to increasing density. A reduction in early survival is
expected to be the first response, followed by an increase
in the age at first calving, then a reduction in reproductive
rates and, lastly, an increase in adult mortality (note:
opinions about this sequence may differ, see Gaillard
et al., 2000). The increase in juvenile deaths at high
densities (reflected in the decline in maternal success),
together with the trend of increasing age at maturity with
increasing density, but the lack of response of ICIs and
265
adult mortality, suggests that the Pilanesberg black rhino
population is responding in accordance with the sequence
expected by Eberhardt (2002).
Considering the response of ‘maternal success’, the
Pilanesberg black rhino population is starting to show
signs of density dependence at its present density of
0.12 animals/km2 . With the first signs appearing at densities above 0.085 rhinos/km2 , K for Pilanesberg would
be approximately 0.17 rhinos/km2 , assuming that density
dependence begins at around K/2 (Caughley, 1977). This
value of K is only valid under the present conditions,
however, and is most probably an over-estimation of the
long term K, since it was derived during a period of high
rainfall. A more conservative estimate would therefore
be closer to the previously estimated and model-predicted
value of K, namely 0.1 rhinos/km2 . A carrying capacity of
0.1 rhinos/km2 is relatively low compared to other reserves
with similar rainfall. Ndumu Game Reserve, for example, supports a density of 0.38 rhinos/km2 (Conway &
Goodman, 1989) and Olduvai and Ngorongoro (Tanzania)
support densities of 0.16 and 0.32 rhinos/km2 , respectively
(Goddard, 1967). Pilanesberg’s comparatively low carrying capacity may be due to a number of factors identified
by Adcock et al. (1998), namely: the poor nutrient status of
the Park, the extent of inaccessible steep and rocky terrain,
the influence of frost on browse quality, competition with
other browsers and the length of the dry season.
From this study, the assessment of population
performance in response to increasing densities and
variable rainfall has proved useful for assessing the
ability of a reserve to support a black rhino population
in the long term. More specifically, two points useful
to the detection of density dependence and the future
conservation of the species are highlighted. First, the
index most sensitive to population density, namely the
percentage of females achieving maternal success, has
been identified. Second, the strong association between
rainfall and population performance, even at low densities,
highlights the need to assess performance during periods
of high rainfall, since only then can a true reflection of
density dependence be gained. The value of maintaining
intensive monitoring programmes cannot be overstated.
Detecting early indications of density dependence will
allow subpopulations to be maintained at densities
well below K, thus maximising population growth
within a conservation strategy based on metapopulation
management.
Acknowledgements
We thank the North West Parks Board for the use of this
data set and for providing accommodation in Pilanesberg
and the staff of Pilanesberg, namely Gus van Dyk, Bruce
Brocket and Raymond Schaller, for all their help. In
addition, we thank Hanna Lindemann and Hans Bjarne
for initiating and maintaining the ear-notching program
and George Phiri for his significant contribution to the
data collection for this project. Without the work done by
these people, this project would not have been possible.
266
H. HRABAR AND J. T. DU TOIT
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