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A genetic index for stripe-pattern
reduction in the zebra:
the quagga project
Rochelle Parsons
1
, Colleen Aldous-Mycock
1
*
& Michael R. Perrin
2
1
School of Biochemistry, Genetics, Microbiology and Plant Pathology, University of KwaZulu-Natal,
Private Bag X01, Scottsville, 3209 South Africa
2
School of Biological and Conservation Sciences, University of KwaZulu-Natal, Scottsville, 3209
Received 15 February 2007. Accepted 21 May 2007
The quagga project aims to breed plains zebra that phenotypically resemble the extinct
quagga (
Equus quagga quagga
), by selective breeding to aggregate desirable characteristics,
particularly a reduced striping pattern. The purpose of this study was to produce a genetic
selection index to improve stripe-pattern reduction, and hence to produce an efficient and
objective selective breeding protocol, which will hopefully be of use in future selection
experiments. From images of selectively bred zebras, striping ratios for three regions were
calculated. Correlations of parent-offspring relationships resulted in narrow-sense
heritabilities. Data from two regions (R1 and R3) were used to create an index to improve
selection and reduce striping. The index
I
= 3.1875 (R1) + 4.8134 (R3) and the response to
selection using the index,
R
= 0.6047 i.
Key words:
Equus quagga,
quagga, stripe pattern, genetic index.
INTRODUCTION
The extinct quagga
The quagga (
Equus quagga quagga
)wasa
zebra-like equid once common in South Africa but
became extinct approximately 100 years ago
due to over-hunting or planned extermination by
colonists and competition with livestock. The
quagga was morphologically divergent in coat
colour from all zebras (Leonard
et al.
2005), having
brown zebra-like stripes only on the anterior half of
its body, while the hind quarters were almost solid
brown in colour (Fig. 1c).
The core of the quagga’s geographical range
was the semi-arid, temperate Karoo where it
co-occurred with the plains zebra (
E.burchelli
)ina
narrow belt of overlap north of the Orange river,
and Hartmann’s mountain zebra (
Equus zebra
hartmannae
) in neighbouring Namibia to the
northwest (Hack
et al.
2002).
In order to resolve the controversy surrounding
the taxonomic status of the quagga, immunological
and molecular comparative analyses based on
mitochondrial DNA from preserved skins were
performed. They demonstrated that the quagga is
not a distinct species but one of several subspecies
of the plains zebra (Higuchi
et al.
1984, 1987;
Lowenstein & Ryder 1985).
The quagga haplotypes investigated are closely
related to one another with an average sequence
divergence of 0.6% (Leonard
et al.
2005). These
data support a close affiliation between the
quagga and the plains zebra, since the South African
plains zebra differs from the quagga by 1.5% and
from other plains zebras by 2.4% in a 395 bp mito
-
chondrial region (Leonard
et al.
2005). The results
were derived from the mtDNA homologies and
substitution rates of 10
–8
substitutions/site/year
(Oakenfull
et al.
2000). This suggests divergence
occurred between the quagga and plains zebra in
the Pleistocene, during the glacial maximum
around 120 000–290 000 years before present
(Hewitt 2000).
There are several hypotheses that may explain
the differences in coat colouration (Fig. 1) (Ruxton
2000). Each infers some fitness benefit of the
striped pattern, resulting from differential selection
and subsequent evolutionary change in favour
of stripes. The hypotheses relate to predator
avoidance, social fitness, thermoregulation and
protection from tsetse flies. Cloudsley-Thompson
(1984) proposed that differential striping patterns
segregated the subspecies while Morris (1990)
suggested the stripes serve as a mechanism for
intraspecies recognition and herd cohesion (Morris
1990). It has been suggested striping affords
South African Journal of Wildlife Research 37(2): 00–00 (October 2007)
*To whom correspondence should be addressed.
E-mail: aldousC@ukzn.ac.za
protection against attack by tsetse flies and disease
transmission. Since the quagga occurred outside
the range of the tsetse fly, it has been argued that
striping had no selection value in terms of tsetse fly
avoidance (Waage 1981).
Bennet (1980) proposed that the distinctive coat
colour of the quagga appeared relatively quickly
(although she regarded the quagga’s closest
relative as
E. caballus
and not
E. burchelli
). Extant
plains zebras show a geographical gradient in
progressive stripe reduction from north to south.
This may be correlated with habitat changes, with
the most southerly populations being adapted to
open country; the quagga represented the extreme
limit of the trend (Rau 1978).
Individuals (museum specimens and photo
-
graphs) vary in the degree to which they show
‘classic’ quagga features, particularly the lack of
stripes and the shade of brown colouration on the
hind quarters. The proposed rapid evolution of
coat colour and pattern could perhaps be explained
by genetic drift, the disruption of gene flow by
geographical isolation and/or an adaptive response
to a drier habitat (Leonard
et al.
2005).
The Quagga Project
The key aim of the Quagga Project is to breed a
zebra phenotypically identical to the extinct
quagga from populations of plains zebras. (The
Quagga Project 2006). There is considerable
intraspecific variability in the pelts of the preserved
quagga museum specimens, and in populations of
plains zebra. Some plains zebra exhibit quagga-
like characteristics (traits) including shades of
brown in their colouring, a reduction in striping and
a minor flare in the tail. The project aims to select
these traits into individuals which would be
quagga-like.
In 1987, nine zebras (from a population of 2500
zebras) were selected based on their quagga-like
traits and captured at the Etosha National Park
to start the breeding programme. Later, other
breeding stocks were selected and captured from
Etosha and Zululand. This original stock has been
kept in eleven localities in the Western Cape.
Currently, the quagga project comprises 150
individuals (The Quagga Project 2006), including
some of the originally captured breeding stock as
well as up to four generations of offspring. The
2 South African Journal of Wildlife Research Vol. 37, No. 2, October 2007
Fig. 1. Striping variation between the zebra species and quagga. a, Mountain zebra,
Equus zebra
; b, Grevy’s zebra
E. greveyi
; c, Burchell’s zebra,
E. burchelli
; d, quagga,
E. quagga
(the London Zoo mare, the only quagga to have
been photographed alive, by Frederick York (1870))
.
c
b
a
d
success of the project is seen in its third and fourth
generation foals in terms of stripe reduction
(Fig. 2). However, the brown coat colour has not
been as successfully bred into the population.
The aim of this investigation was to produce a
striping index to facilitate further stripe reduction
through selective breeding. It necessitated develop
-
ing a new method of quantifying striping because
existing methods, of visual and manual stripe
counting, are time-consuming and may not offer a
high degree of reliability.
METHODS
The specific trait investigated was the ratio of
striped to non-striped areas of each zebra. The
data analysis was based on digital images of all
the animals in the studbook. The studbook was
completed at the beginning of 2006 by the late R.E.
Rau of the Iziko South African Museum. Informa
-
tion on the parentage of each individual was in
-
cluded where known. For each animal, a left, right
and hind view were included in the studbook.
Data analysis
For each individual, the view (left or right view)
with the greatest resolution was chosen for the
analysis of striping ratios. To ensure that the left
and right view ratios did not differ significantly from
each other, a random sample of 15 individuals
were chosen (10%) from the population. Their left
and right ratios compared as a control.Each image
was digitally enhanced using software ArcSoft
PhotoStudio™ to enhance the contrasts of the
striping patterns. To compile the striping ratios, the
software package AnalySIS
®
Pro 3.2 (build 689)
Soft Imaging System was used.
In order to define the ratio of striped to non-striped
areas a macro imaging procedure was created,
and programmed to apply a repetitive process
when a new image was added. The process
included setting the region of interest; Region 1:
the shoulder to rear; 2, the rear to the mid section;
3, the rump (Fig. 3).
After setting the region of interest, area measure
-
ment was defined first for the non-striped areas
and then for the total area. The non-striped areas
were identified by adjusting contrast and colour
intensity thresholds to exclude the maximum
amount of brown and black stripes. The striped
area was calculated as the non-striped area
subtracted from the total area. For each individual
the ratio of striped to total area was calculated.The
data unit for area measurement (pixel
2
),allowed for
any image size to be used and ratios to be
calculated.
Statistical analysis and index calculations
The phenotypic variance for each region was
calculated using GenStat
®
(v. 9.1). Comparative
analysis was performed to check whether any of
the data for the three regions were redundant.
It showed that regions 1 and 2 were redundant,
leaving regions 1 and 3 for constructing the index.
The mean of each parent’s offspring was calcu
-
lated (appendices A and B). The regression com
-
parison of (a) dams to their offspring means and
(b) sires to their offspring means was performed as
a control to test for any significant difference
between male and female parents.A regression of
combined parents (P) to offspring (O) was used to
deduce heritability estimates. Heritability can be
defined statistically as the proportion of phenotypic
variance attributable to genetic variance or more
commonly as the extent to which genetic individual
differences contribute to individual differences in
phenotype (Falconer & Mackay 1996). The slope
of the linear regression (
b
OP
) estimates heritability
as:
b
OP
= ½
h
2
. (1)
The heritabilities for both regions were calculated.
The phenotypic correlation (
r
P
) and genetic corre-
lation (
r
A
) values were required for the index to
be constructed. The phenotypic correlation is an
estimate of the association between visible
characteristics while the genetic correlation is the
correlation between breeding values. Correlation
estimates were calculated using Genstat (ver
-
sion 9.1).
r
p
=
cov
)
p(13
p1 p3
σσ
, (2)
where
cov
P(13)
is the covariance between regions 1
and 3 in parents and σ
P
is the standard deviation for
parents within areas 1 and 3, respectively.
r
A
=
cov
cov cov
OP(13)
OP(1) OP(3)
, (3)
where
cov
OP(13)
is the covariance between parent
region 1 and offspring and offspring region 3,
cov
OP(1)
is the covariance between parent and off
-
spring in region 1 and
cov
OP(3)
is the covariance
between parent and offspring in region 3. Co
-
variance is the measure of how much two traits
vary together.
The breeding objective in this study was striping
reduction. The breeding value is evaluated as a
Parsons
et al.
: Genetic index for stripe-pattern reduction in the zebra 3
composite of all striping characters evaluated
when trying to calculate the score (or index). It was
calculated separately for each individual.
In order to generate the index, the solutions of
the
b
coefficients in the following equations were
used as the coefficients for the trait improvement:
b
1
P
11
+
b
3
P
13
=
a
1
A
11
+
a
3
A
13
b
1
P
31
+
b
3
P
33
=
a
1
A
31
+
a
3
A
33
, (4)
where
P
ii
is the phenotypic variance for each
region and
P
ij
is the covariance between regions.
A
ii
is the additive variance of the regions and
A
ij
is the additive covariance between regions (
i,j
=1,
3):
A
ii
=
h
i
2
σ
P
i
2
(5)
A
ij
=
r
A
h
i
h
j
σ
P
i
σ
P
j
. (6)
The economic weights may reflect preferences
or simply arbitrarily fixed values. Ideally, an
economic weight of a single trait should reflect the
marginal benefit from a one unit improvement.
Economic weights (
a
) were assigned as the
inverse of the phenotypic standard deviation of
each region i.e.
a
1
=
1
/σ
1
and
a
3
=
1
/σ
3
(Falconer &
Mackay 1996).
By solving the above equation simultaneously
for the
b
coefficients, the index was formed by
substitution. The variance of the index (σ
I
2
)was
then calculated as:
σ
I
2
=
b
1
(
a
1
A
11
+
a
3
A
13
) +
b
3
(
a
1
A
31
+
a
3
A
33
) . (7)
The response to selection (
R
) based on the
index was then be predicted by:
R
= σ
I
i . (8)
where
i
is the intensity of selection (Falconer &
Mackay 1996).
4 South African Journal of Wildlife Research Vol. 37, No. 2, October 2007
Fig. 2. The most quagga-like plains zebra foal from the quagga project (Courtesy of the Quagga Project).
Fig.3.The selected regions for data analysis;a, Region 1:shoulder to rear;b, Region 2: mid to rear;c Region 3:rump.
abc
RESULTS
For regression analysis for the calculation of index
coefficients, data for 22 dams and 16 sires (with a
minimum of two offspring) were used. Since there
were no significant differences (
P
< 0.0001)
between the left and right coat patterns, further
analyses were done using the flank with the greater
resolution. The regression controls for sires
(Appendix A) and dams (Appendix B) provided
regression coefficients of 0.2306 and 0.2151,
respectively for region 1 (Fig. 4). The effect of
gender on coat patterning was insignificant and so
regressions were calculated using the combined
data set.
For region 1 the mean striping ratio was 0.411,
with a minimum ratio of 0.93 and a maximum ratio
of 0.630. For region 3 the mean striping ratio was
0.396 with a minimum ratio of 0.056 and a maxi
-
mum ratio of 0.668.The regression coefficient was
0.2171 for region 1 and 0.2705 for region 3 (Fig. 5).
The heritability for each region was calculated
from these values using equation 1;
h
1
2
and
h
3
2
are
0.4342 and 0.5410, respectively. The phenotypic
variance of region 1 (σ
2
P1
or
P
11
) is 0.0098 and of
region 3 (σ
2
P3
or
P
33
) is 0.0134. The phenotypic
covariance of regions 1 and 3 (
cov
P(13)
equivalent
to
P
13
=
P
31
) is 0.0064. The additive variance of
region 1 (
A
11
) and region 3 (
A
33
), derived from
Equation 5, are 0.0042 and 0.0072, respectively.
The additive correlation (
r
A
) was calculated as
0.3953 (Equation 3) and the additive covariance
between the regions (
A
13
=
A
31
) was 0.0021 (Equa-
tion 6).
The economic weightings a
1
and a
3
were estimated
at 10.1272 and 8.6457, respectively, and therefore
b
1
and b
3
equate to 3.1875 and 4.8134, respec
-
tively. These calculations yield an overall index
equation of:
I
= 3.1875 (R1) + 4.8134 (R3) .
The variance of the index (σ
I
2
) is 0.6048 (Equa
-
tion 7), and the response to selection equation is
then given by
R
= 0.6047
i
.
DISCUSSION
The results demonstrate a strong correlation
between parents and offspring with reference to
reduction in stripe pattern. The absence of any
significant difference between gender and parent–
offspring correlation in coat pattern indicate that
paternal and maternal effects are equivalent in this
regard. Relatively large variances suggest there is
scope for further stripe pattern reduction through
continued selective breeding. The heritability esti
-
mates for stripe pattern reduction in both regions
are particularly high, suggesting that selection
Parsons
et al.
: Genetic index for stripe-pattern reduction in the zebra 5
Fig. 4. Regression of offspring on sire and mare values for shoulder to rear striping (region 1).
based on phenotypic variation would be success-
ful, because the phenotype is a good indicator of
the animals’ inherent breeding potential. Results
show a definite trend towards stripe pattern reduc-
tion. The high heritabilities for striping ratio could
be due to the relatively small number of animals it
was possible to include in this study.
Many examples of heritability of other quantitative
traits by both natural and artificial selection for coat
colour can be found in the literature (Andersson
2001). Mathematical models have also been
produced which show that relatively large coat
colour pattern changes can occur with only small
changes in the model parameters (Murray 2003).
The index is a prediction of breeding success in
relation to reducing striping, and shows the rump
to be the most important region when attempting
to achieve the quagga-like phenotype. However,
for successful classification of each animal, both
characters should contribute to the index to provide
a value upon which any further selection strategies
are based. The variance of the index should be
used to predict the response to selection when
the intensity of selection is known. It can also be
compared with the response through simple selec
-
tion to estimate the index’s efficiency.
The data presented above will aid selective
breeding in the quagga project population.
However, pleiotrophy and epistasis may also be
in play and have not been considered in this study.
For the purpose of producing a simple index it was
assumed that the trait is quantitative with additive
gene action and perhaps only dominance deviations
were present.
Important progress in the data analysis procedure
was made in this study.The macro produced along
with the effective use of the AnalySIS
®
Imaging
software allows the breeder to obtain a photograph
of the animal from either left or right view.When the
‘cleaned’ image is entered it yields the relevant
data within minutes. When the data are entered
into the prepared Microsoft Excel spreadsheet, the
individual’s index value is calculated automatically.
This simple and effective method provides accurate
striping ratios from the formula spreadsheet with
-
out the use of any manual, mathematical or statisti
-
cal operations.
In conclusion, the index will allow the Quagga
Project to simplify its selective breeding protocol
and to reduce the striping pattern in the study
population. It could be appropriate and useful to
use such data collection and indexing methods for
quantifiable phenotypic traits in other mammals.
To further explore stripe pattern reduction, it is
necessary to study the interaction and number of
genes involved in the process. The results of such
6 South African Journal of Wildlife Research Vol. 37, No. 2, October 2007
Fig. 5. Regression of offspring on single parent values for shoulder to rear striping (region 1) and rump (region 3).
a study together with the index generated here will
aid achieving the project’s objectives.
ACKNOWLEDGEMENTS
Micheal Knight and the rest of the team of the
Quagga Breeding Project are thanked for the data
and their support of this research. We thank the
technical staff of the Centre for Electron Micros
-
copy of University of KwaZulu-Natal for their help
with image analysis.Carl Roux from the University
of Pretoria is also thanked for statistical advice.
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: Genetic index for stripe-pattern reduction in the zebra 7
Corresponding editor: M.I. Cherry
Appendix A
Sire parentage and offspring data for body regions 1 and 3. (Y.O.B. = year of birth.)
Sire Dam Offspring Y.O.B. 1 3
Alex Melanie Shaun 1992 0.476 0.403
Melanie Luke 1995 0.431 0.439
Melanie Leius 1999 0.407 0.530
Howey Bernard 1999 0.493 0.500
Charlene Marilyn 1993 0.432 0.349
Sokkies Jeanetta 1993 0.277 0.252
Melanie Nicola 2000 0.476 0.360
Howey Erina 2000 0.414 0.477
Melanie Amanda 2001 0.306 0.452
Brenda Niki 1992 0.459 0.222
Charlene Vernon 1991 0.527 0.339
Sokkies Emse 1991 0.454 0.288
Melanie Libby 1993 0.599 0.509
Charlene Quanashi 1994 0.300 0.265
Sokkies Simone 1994 0.326 0.245
Sokkies David 1995 0.459 0.293
8 South African Journal of Wildlife Research Vol. 37, No. 2, October 2007
Appendix A (
continued
)
Sire Dam Offspring Y.O.B. 1 3
Melanie Dierdre 1996 0.590 0.302
Sokkies Eric 1996 0.393 0.154
Melanie Lois 1997 0.624 0.363
Melanie Lexus 1999 0.397 0.499
Melanie Manie 2002 0.575 0.503
Howey Hans 2002 0.444 0.373
Erina Kerry 2003 0.513 0.529
Melanie Koos 2003 0.226 0.310
Melanie Himma 2004 0.476 0.355
Mean 0.443 0.372
Allan Reina Paul 1993 0.340 0.390
Lulu Mariette 1994 0.324 0.266
Betty Bad luck 1989 0.654 0.384
Betty Brian 1991 0.525 0.360
Lulu Hennie 1992 0.389 0.512
Betty Vossi 1992 0.472 0.382
Lulu Monica 1993 0.352 0.325
Betty Dale 1993 0.464 0.348
Lulu Chris 1995 0.335 0.278
Lulu Mark 1996 0.331 0.414
Simone John 1997 0.196 0.280
Monica Matz 1997 0.350 0.144
Lulu George 1998 0.398 0.369
Mean 0.395 0.342
Albert Brenda Mike 1993 0.566 0.474
Tandi Morne 1994 0.269 0.341
Emse Theresa 1994 0.448 0.420
Try-Me Denise 1997 0.603 0.574
Tandi Ammy 1990 0.525 0.366
Tandi Zain 1991 0.496 0.308
Tandi Dianne 1992 0.493 0.621
Tandi Fransi 1993 0.587 0.538
Brenda Estelle 1994 0.601 0.338
Celeste Cima 1995 0.500 0.545
Try-Me Andreas 1996 0.589 0.362
Brenda Erika 1996 0.458 0.216
Tandi Barbara-Anne 1996 0.430 0.345
Celeste Medee 1998 0.415 0.560
Mean 0.499 0.429
Megavolt Charlene Ziggi 1999 0.421 0.524
Mariette Zephyr 1997 0.458 0.416
Charlene Allmi 2000 0.514 0.508
Charlene Ginny 2002 0.482 0.537
Charlene Storm 1996 0.525 0.290
Charlene Kwaze 1998 0.482 0.397
Mean 0.480 0.445
Shaun Jeanetta Louis 1997 0.331 0.398
Monica Ryan 1999 0.406 0.521
Jeanetta Lindsay 1999 0.420 0.346
Rene Johan 2003 0.394 0.313
Monica Leslie 2000 0.425 0.289
Jeanetta Caroline 1998 0.628 0.350
Monica Elizabeth 2001 0.510 0.385
Jeanetta Whity 2002 0.423 0.404
Parsons
et al.
: Genetic index for stripe-pattern reduction in the zebra 9
Appendix A (
continued
)
Sire Dam Offspring Y.O.B. 1 3
Susan Nina 2003 0.445 0.229
Mean 0.442 0.359
Fanie Ricky Leon 1998 0.307 0.421
Ricky Canya 1996 0.525 0.231
Ricky Erica 2000 0.299 0.245
Ricky Stephan 1997 0.331 0.337
Ricky Teib 1999 0.286 0.319
Ricky Deon 2001 0.456 0.411
Ricky Ralph 2002 0.482 0.391
Mean 0.384 0.336
Hennie Theresa Etienne 1998 0.363 0.316
Theresa Lal 2000 0.402 0.433
Mean 0.383 0.375
Luke Lulu Duncan 2001 0.299 0.203
Mariette Cedric 2001 0.492 0.288
Amanda Gary 2003 0.339 0.234
Mariette Tracy 2000 0.394 0.378
Lulu Marjean 2000 0.361 0.538
Zephyr Truida 2002 0.418 0.498
Mariette Joy 2002 0.250 0.215
Lulu Mientie 2002 0.357 0.411
Nicola Nico 2003 0.433 0.400
Zephyr Butch 2003 0.366 0.459
Mariette EricH 2003 0.496 0.583
Amanda Robin 2004 0.373 0.424
Elizabeth Henry 2005 0.185 0.056
Mean 0.366 0.361
Ike Marcelle Karl 2002 0.320 0.449
Marcelle Jaunie 2003 0.380 0.307
Marcelle Rene 1998 0.380 0.203
Marilyn Stelza 1999 0.367 0.402
Marcelle Susan 1999 0.398 0.289
Marcelle Anine 2001 0.093 0.254
Marilyn Audri 2001 0.416 0.370
Rene Linda 2001 0.309 0.292
Stelza Margaret 2003 0.367 0.274
Marilyn Emilene 2004 0.367 0.398
Marcelle Marlene 2004 0.449 0.347
Mean 0.350 0.326
Paul Try-Me Klaus 2002 0.452 0.503
Try-Me Mandy 1999 0.412 0.381
Try-Me Erna 2000 0.400 0.502
Medee Karen 2001 0.471 0.466
Try-Me Connie 2003 0.531 0.533
Erina Dolly 2003 0.621 0.538
Anine Eddie 2004 0.459 0.462
Mean 0.478 0.483
Leon Ricky Mathews 2004 0.497 0.394
Erica Amico 2004 0.325 0.297
Mean 0.411 0.345
George Leslie Rebecca 2003 0.457 0.536
Jeanetta Tim 2003 0.563 0.581
10 South African Journal of Wildlife Research Vol. 37, No. 2, October 2007
Appendix A (
continued
)
Sire Dam Offspring Y.O.B. 1 3
Monica Thor 2003 0.540 0.613
Mean 0.520 0.577
Mike Barbara-Anne Paddy 2001 0.388 0.433
Theresa Fritz 2002 0.454 0.668
Denise Zebbi 2003 0.584 0.515
Mean 0.475 0.538
Ziggi Charlene Vuyo 2003 0.286 0.329
Charlene Lawrence 2004 0.375 0.328
Mean 0.331 0.328
Etienne Tracy Douw 2003 0.330 0.239
Tracy Frank 2004 0.283 0.381
Mean 0.307 0.310
Lindsay Ricky Benni 2005 0.379 0.408
Erika Jacques 2005 0.396 0.382
Mean 0.388 0.395
Appendix B
Dam parentage and offspring data for body regions 1 and 3. (Y.O.B. = year of birth).
Dam Sire Offspring Y.O.B. Region 1 Region 3
Betty Allan Bad luck 1989 0.654 0.384
Allan Brian 1991 0.525 0.36
Allan Vossi 1992 0.472 0.382
Allan Dale 1993 0.464 0.348
Mean 0.53 0.37
Brenda U Joxi 1988 0.323 0.318
Alex Niki 1991 0.459 0.222
Albert Mike 1993 0.566 0.474
Albert Estelle 1994 0.601 0.338
Albert Erika 1996 0.458 0.216
Mean 0.48 0.31
Tandi Albert Barbara-Anne 1996 0.430 0.345
Albert Ammy 1990 0.525 0.366
Albert Zain 1991 0.496 0.308
Albert Dianne 1992 0.493 0.621
Albert Fransi 1993 0.587 0.538
Albert Morne 1994 0.269 0.341
Mean 0.47 0.42
Lulu Tsjaka Reina 1990 0.340 0.294
Allan Monica 1993 0.352 0.325
Allan Mariette 1994 0.324 0.266
Luke Marjean 2000 0.361 0.538
Allan Hennie 1992 0.389 0.512
Allan Chris 1995 0.335 0.278
Allan Mark 1996 0.331 0.414
Allan George 1998 0.398 0.369
Parsons
et al.
: Genetic index for stripe-pattern reduction in the zebra 11
Appendix B (
continued
)
Sire Dam Offspring Y.O.B. 1 3
Luke Sebastian 1999 0.382 0.322
Luke Duncan 2001 0.299 0.203
Luke Mientie 2002 0.357 0.411
Mean 0.35 0.36
Sokkies Alex Jeanetta 1993 0.277 0.252
Alex Simone 1994 0.326 0.245
Alex Emse 1991 0.454 0.288
Alex David 1995 0.459 0.293
Alex Eric 1996 0.393 0.154
Mean 0.3818 0.246
Melanie Alex Libby 1993 0.599 0.509
Alex Nicola 2000 0.476 0.36
Alex Amanda 2001 0.306 0.452
Alex Shaun 1992 0.476 0.403
Alex Luke 1995 0.431 0.439
Alex Leius 1999 0.407 0.53
Alex Deirdre 1996 0.590 0.302
Alex Lois 1997 0.624 0.363
Alex Lexus 1999 0.397 0.499
Alex Manie 2002 0.575 0.503
Alex Koos 2003 0.226 0.31
Alex Himma 2004 0.476 0.355
Mean 0.47 0.42
Charlene Megavolt Ziggi 1999 0.421 0.524
Alex Marilyn 1993 0.432 0.349
Megavolt Allmi 2000 0.514 0.508
Megavolt Ginny 2002 0.482 0.537
Alex Vernon 1991 0.527 0.339
Alex Quahashi 1994 0.3 0.265
Megavolt Storm 1996 0.5246 0.29
Megavolt Kwaze 1998 0.482 0.397
Ziggi Vuyo 2003 0.286 0.329
Ziggi Lawrence 2004 0.375 0.328
Mean 0.43 0.39
Marilyn Ike Stelza 1999 0.367 0.402
Ike Audri 2001 0.416 0.37
Ike Emilene 2004 0.367 0.398
Mean 0.38 0.39
Monica Shaun Ryan 1999 0.406 0.521
Shaun Leslie 2000 0.425 0.289
Shaun Elizabeth 2001 0.51 0.385
Allan Matz 1997 0.35 0.144
George Thor 2003 0.54 0.613
Mean 0.45 0.39
Jeanetta Shaun Louis 1997 0.331 0.398
Shaun Lindsay 1999 0.42 0.346
Shaun Caroline 1998 0.628 0.35
Shaun Whity 2002 0.423 0.404
George Tim 2003 0.563 0.581
Mean 0.47 0.42
Try-Me Albert Denise 1997 0.603 0.574
Paul Mandy 1999 0.412 0.381
Paul Erna 2000 0.400 0.502
Albert Andreas 1996 0.617 0.362
12 South African Journal of Wildlife Research Vol. 37, No. 2, October 2007
Appendix B (
continued
)
Dam Sire Offspring Y.O.B. Region 1 Region 3
Paul Klaus 2002 0.452 0.503
Paul Connie 2003 0.531 0.533
Mean 0.50 0.48
Ricky Fanie Leon 1998 0.307 0.421
Fanie Erica 2000 0.299 0.245
Fanie Canya 1996 0.525 0.231
Fanie Stephan 1997 0.331 0.337
Fanie Teib 1999 0.286 0.319
Fanie Deon 2001 0.456 0.411
Fanie Ralph 2002 0.482 0.391
Leon Mathews 2004 0.497 0.394
Lindsay Benni 2005 0.379 0.408
Mean 0.40 0.35
Howey Alex Bernard 1999 0.493 0.5
Alex Erina 2000 0.414 0.477
Alex Hans 2002 0.444 0.373
Mean 0.45 0.45
Mariette Luke Cedric 2001 0.492 0.288
Megavolt Zephyr 1997 0.458 0.416
Luke Tracy 2000 0.394 0.378
Luke Joy 2002 0.25 0.215
Luke EricH 2003 0.496 0.583
Mean 0.42 0.38
Marcelle ? Genis 1996 0.318 0.279
Ike Karl 2002 0.32 0.449
Ike Jaunie 2003 0.38 0.307
Ike Rene 1998 0.38 0.203
Ike Susan 1999 0.398 0.289
Ike Anine 2001 0.093 0.254
Mean 0.31 0.30
Theresa Hennie Etienne 1998 0.363 0.316
Hennie Lal 2000 0.402 0.433
Mike Fritz 2002 0.454 0.668
Mean 0.41 0.47
Zephyr Luke Truida 2002 0.418 0.498
Luke Butch 2003 0.366 0.459
Mean 0.39 0.48
Rene Shaun Johan 2003 0.394 0.313
Ike Linda 2001 0.309 0.292
Shaun Griet 2004 0.41 0.277
Mean 0.37 0.29
Susan Shaun Nina 2003 0.445 0.229
Shaun Rosa 2004 0.393 0.389
Mean 0.42 0.31
Tracy Etienne Douw 2003 0.33 0.239
Etienne Frank 2004 0.283 0.381
Mean 0.31 0.31
Amanda Luke Gary 2003 0.339 0.234
Luke Robin 2004 0.373 0.424
Mean 0.36 0.33
Celeste Albert Cima 1995 0.5 0.545
Albert Medee 1998 0.415 0.56
Mean 0.46 0.55
