1. science
2. biology
3. breeding

A genetic index for stripe-pattern
reduction in the zebra:
the quagga project
Rochelle Parsons , Colleen Aldous-Mycock * & Michael R. Perrin
1
1
1
2
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, genetic index, quagga, stripe pattern.
INTRODUCTION
The extinct quagga
The quagga (Equus quagga quagga) was a
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) in a
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.
*To whom correspondence should be addressed.
E-mail: [email protected]
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 mitochondrial region (Leonard et al. 2005). The results
were derived from the mtDNA homologies and
–8
substitution rates of 10 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): 105–116 (October 2007)
106
South African Journal of Wildlife Research Vol. 37, No. 2, October 2007
a
b
c
d
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)).
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 photographs) 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 quaggalike 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
Parsons et al.: Genetic index for stripe-pattern reduction in the zebra
107
Fig. 2. The most quagga-like plains zebra foal from the quagga project (Courtesy of the Quagga Project).
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 developing 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.
a
b
Rau of the Iziko South African Museum. Information
on the parentage of each individual was included
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.
c
Fig. 3. The selected regions for data analysis; a, Region 1: shoulder to rear; b, Region 2: mid to rear; c, Region 3: rump.
108
South African Journal of Wildlife Research Vol. 37, No. 2, October 2007
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 measurement 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
2
data unit for area measurement (pixel ),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 calculated (appendices A and B). The regression comparison 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 (bOP) estimates heritability
as:
bOP = ½h 2 .
(1)
The heritabilities for both regions were calculated.
The phenotypic correlation (rP) and genetic correlation (rA) 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 (version 9.1).
rp =
cov p(13 )
σ p1σ p3
,
(2)
where covP(13) is the covariance between Regions 1
and 3 in parents and σP is the standard deviation for
parents within areas 1 and 3, respectively.
cov OP(13)
,
(3)
rA =
cov OP(1)cov OP(3)
where covOP(13) is the covariance between parent
Region 1 and offspring and offspring Region 3,
covOP(1) is the covariance between parent and offspring in Region 1 and covOP(3) is the covariance
between parent and offspring in Region 3. Covariance 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
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:
b1P11 + b3P13 = a1A11 + a3A13
b1P31 + b3P33 = a1A31 + a3A33 ,
(4)
where Pii is the phenotypic variance for each
region and Pij is the covariance between regions.
Aii is the additive variance of the regions and Aij
is the additive covariance between regions (i,j = 1,
3):
Aii = hi2σPi2
(5)
Aij = r Ah i h j σ Pi σ Pj .
(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
1
1
each region i.e. a1 = /σ1 and a3 = /σ3 (Falconer &
Mackay 1996).
By solving the above equation simultaneously
for the b coefficients, the index was formed by
2
substitution. The variance of the index (σI ) was
then calculated as:
σI = b1 (a1A11 + a3A13) + b3 (a1A31 + a3A33) .
2
(7)
The response to selection (R ) based on the
index was then be predicted by:
R = σ Ii ,
(8)
where i is the intensity of selection (Falconer &
Mackay 1996).
Parsons et al.: Genetic index for stripe-pattern reduction in the zebra
109
Fig. 4. Regression of offspring on sire and mare values for shoulder to rear striping (Region 1).
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 maximum 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
2
2
from these values using equation 1; h1 and h3 are
0.4342 and 0.5410, respectively. The phenotypic
variance of Region 1 (σ2P1 or P11) is 0.0098 and of
Region 3 (σ2P3 or P33) is 0.0134. The phenotypic
covariance of Regions 1 and 3 (covP(13) equivalent
to P13 = P31) is 0.0064. The additive variance of
Region 1 (A11) and Region 3 (A33), derived from
Equation 5, are 0.0042 and 0.0072, respectively.
The additive correlation (rA) was calculated as
0.3953 (Equation 3) and the additive covariance
between the regions (A13 = A31) was 0.0021 (Equation 6).
The economic weightings a1 and a3 were estimated
at 10.1272 and 8.6457, respectively, and therefore
b1 and b3 equate to 3.1875 and 4.8134, respectively. These calculations yield an overall index
equation of:
I = 3.1875 (R1) + 4.8134 (R3) .
The variance of the index (σI2) is 0.6048 (Equation 7), and the response to selection equation is
then given by R = 0.6047i.
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 estimates for stripe pattern reduction in both regions
are particularly high, suggesting that selection
110
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).
based on phenotypic variation would be successful, because the phenotype is a good indicator of
the animals’ inherent breeding potential. Results
show a definite trend towards stripe pattern reduction. 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 selection 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 without the use of any manual, mathematical or statistical 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
Parsons et al.: Genetic index for stripe-pattern reduction in the zebra
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 Microscopy of University of KwaZulu-Natal for their help
with image analysis. Carl Roux from the University
of Pretoria is also thanked for statistical advice.
REFERENCES
ANDERSSON, L. 2001. Genetic dissection of phenotypic diversity in farm animals. Nat. Rev. Genet. 2:
130–138.
BENNET, D.K. 1980. Stripes do not a zebra make, Part I:
A cladistic analysis of equus. Syst. Zool. 29: 272–287.
CLOUDSLEY-THOMPSON, J.L. 1984. How the zebra
got his stripes – new solutions to an old problem. Biologist 31: 226–228.
FALCONER, D.S. & MACKAY, T.F. 1996. Introduction to
quantitative genetics (4th edn). Longman, New York.
HACK, M.A., EAST, R., & RUBENSTEIN, D.I. 2002.
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burchellii). In: P.D. Moehlman (ed.), Equids: zebras,
asses and horses. Status survey and conservation
action plan, IUCN/SSC Equid Specialist Group,
IUCN, Gland, Switzerland.
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ice ages. Nature 405: 907–913.
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RYDER, O.A. & WILSON, A.C. 1984. DNA se-
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quences from the quagga, an extinct member of the
horse family. Nature 312: 282–284.
HIGUCHI, R.G., WRISCHNIK, L.A., OAKES, E.,
GEORGE, M., TONG, B. & WILSON, A.C. 1987.
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and extent of post-mortem change. J. Mol. Evol. 25:
283–287.
LEONARD, J.A. et al. 2005. A rapid loss of stripes: the
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LOWENSTEIN, J.M. & RYDER, O.A. 1985. Immunological systematics of the extinct quagga (Equidae).
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‘The Quagga Project’. Retrieved 15 September 2006
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WAAGE, J.K. 1981. How the zebra got its stripes – biting
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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.
Region 1
Region 3
Alex
Melanie
Melanie
Melanie
Howey
Charlene
Sokkies
Melanie
Howey
Melanie
Brenda
Charlene
Sokkies
Melanie
Charlene
Sokkies
Sokkies
Shaun
Luke
Leius
Bernard
Marilyn
Jeanetta
Nicola
Erina
Amanda
Niki
Vernon
Emse
Libby
Quanashi
Simone
David
1992
1995
1999
1999
1993
1993
2000
2000
2001
1992
1991
1991
1993
1994
1994
1995
0.476
0.431
0.407
0.493
0.432
0.277
0.476
0.414
0.306
0.459
0.527
0.454
0.599
0.300
0.326
0.459
0.403
0.439
0.530
0.500
0.349
0.252
0.360
0.477
0.452
0.222
0.339
0.288
0.509
0.265
0.245
0.293
112
South African Journal of Wildlife Research Vol. 37, No. 2, October 2007
Appendix A (continued )
Sire
Dam
Offspring
Y.O.B.
Region 1
Region 3
Melanie
Sokkies
Melanie
Melanie
Melanie
Howey
Erina
Melanie
Melanie
Dierdre
Eric
Lois
Lexus
Manie
Hans
Kerry
Koos
Himma
Allan
Reina
Lulu
Betty
Betty
Lulu
Betty
Lulu
Betty
Lulu
Lulu
Simone
Monica
Lulu
Paul
Mariette
Bad luck
Brian
Hennie
Vossi
Monica
Dale
Chris
Mark
John
Matz
George
Albert
Brenda
Tandi
Emse
Try-Me
Tandi
Tandi
Tandi
Tandi
Brenda
Celeste
Try-Me
Brenda
Tandi
Celeste
Mike
Morne
Theresa
Denise
Ammy
Zain
Dianne
Fransi
Estelle
Cima
Andreas
Erika
Barbara-Anne
Medee
Megavolt
Charlene
Mariette
Charlene
Charlene
Charlene
Charlene
Ziggi
Zephyr
Allmi
Ginny
Storm
Kwaze
Shaun
Jeanetta
Monica
Jeanetta
Rene
Monica
Jeanetta
Monica
Jeanetta
Louis
Ryan
Lindsay
Johan
Leslie
Caroline
Elizabeth
Whity
1996
1996
1997
1999
2002
2002
2003
2003
2004
Mean
1993
1994
1989
1991
1992
1992
1993
1993
1995
1996
1997
1997
1998
Mean
1993
1994
1994
1997
1990
1991
1992
1993
1994
1995
1996
1996
1996
1998
Mean
1999
1997
2000
2002
1996
1998
Mean
1997
1999
1999
2003
2000
1998
2001
2002
0.590
0.393
0.624
0.397
0.575
0.444
0.513
0.226
0.476
0.443
0.340
0.324
0.654
0.525
0.389
0.472
0.352
0.464
0.335
0.331
0.196
0.350
0.398
0.395
0.566
0.269
0.448
0.603
0.525
0.496
0.493
0.587
0.601
0.500
0.589
0.458
0.430
0.415
0.499
0.421
0.458
0.514
0.482
0.525
0.482
0.480
0.331
0.406
0.420
0.394
0.425
0.628
0.510
0.423
0.302
0.154
0.363
0.499
0.503
0.373
0.529
0.310
0.355
0.372
0.390
0.266
0.384
0.360
0.512
0.382
0.325
0.348
0.278
0.414
0.280
0.144
0.369
0.342
0.474
0.341
0.420
0.574
0.366
0.308
0.621
0.538
0.338
0.545
0.362
0.216
0.345
0.560
0.429
0.524
0.416
0.508
0.537
0.290
0.397
0.445
0.398
0.521
0.346
0.313
0.289
0.350
0.385
0.404
Parsons et al.: Genetic index for stripe-pattern reduction in the zebra
113
Appendix A (continued )
Sire
Dam
Offspring
Y.O.B.
Region 1
Region 3
Susan
Nina
Fanie
Ricky
Ricky
Ricky
Ricky
Ricky
Ricky
Ricky
Leon
Canya
Erica
Stephan
Teib
Deon
Ralph
Hennie
Theresa
Theresa
Etienne
Lal
Luke
Lulu
Mariette
Amanda
Mariette
Lulu
Zephyr
Mariette
Lulu
Nicola
Zephyr
Mariette
Amanda
Elizabeth
Duncan
Cedric
Gary
Tracy
Marjean
Truida
Joy
Mientie
Nico
Butch
EricH
Robin
Henry
Ike
Marcelle
Marcelle
Marcelle
Marilyn
Marcelle
Marcelle
Marilyn
Rene
Stelza
Marilyn
Marcelle
Karl
Jaunie
Rene
Stelza
Susan
Anine
Audri
Linda
Margaret
Emilene
Marlene
Paul
Try-Me
Try-Me
Try-Me
Medee
Try-Me
Erina
Anine
Klaus
Mandy
Erna
Karen
Connie
Dolly
Eddie
Leon
Ricky
Erica
Mathews
Amico
George
Leslie
Jeanetta
Rebecca
Tim
2003
Mean
1998
1996
2000
1997
1999
2001
2002
Mean
1998
2000
Mean
2001
2001
2003
2000
2000
2002
2002
2002
2003
2003
2003
2004
2005
Mean
2002
2003
1998
1999
1999
2001
2001
2001
2003
2004
2004
Mean
2002
1999
2000
2001
2003
2003
2004
Mean
2004
2004
Mean
2003
2003
0.445
0.442
0.307
0.525
0.299
0.331
0.286
0.456
0.482
0.384
0.363
0.402
0.383
0.299
0.492
0.339
0.394
0.361
0.418
0.250
0.357
0.433
0.366
0.496
0.373
0.185
0.366
0.320
0.380
0.380
0.367
0.398
0.093
0.416
0.309
0.367
0.367
0.449
0.350
0.452
0.412
0.400
0.471
0.531
0.621
0.459
0.478
0.497
0.325
0.411
0.457
0.563
0.229
0.359
0.421
0.231
0.245
0.337
0.319
0.411
0.391
0.336
0.316
0.433
0.375
0.203
0.288
0.234
0.378
0.538
0.498
0.215
0.411
0.400
0.459
0.583
0.424
0.056
0.361
0.449
0.307
0.203
0.402
0.289
0.254
0.370
0.292
0.274
0.398
0.347
0.326
0.503
0.381
0.502
0.466
0.533
0.538
0.462
0.483
0.394
0.297
0.345
0.536
0.581
114
South African Journal of Wildlife Research Vol. 37, No. 2, October 2007
Appendix A (continued )
Sire
Dam
Offspring
Y.O.B.
Region 1
Region 3
Monica
Thor
Mike
Barbara-Anne
Theresa
Denise
Paddy
Fritz
Zebbi
Ziggi
Charlene
Charlene
Vuyo
Lawrence
Etienne
Tracy
Tracy
Douw
Frank
Lindsay
Ricky
Erika
Benni
Jacques
2003
Mean
2001
2002
2003
Mean
2003
2004
Mean
2003
2004
Mean
2005
2005
0.540
0.520
0.388
0.454
0.584
0.475
0.286
0.375
0.331
0.330
0.283
0.307
0.379
0.396
0.613
0.577
0.433
0.668
0.515
0.538
0.329
0.328
0.328
0.239
0.381
0.310
0.408
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
Allan
Allan
Allan
Bad luck
Brian
Vossi
Dale
Brenda
U
Alex
Albert
Albert
Albert
Joxi
Niki
Mike
Estelle
Erika
Tandi
Albert
Albert
Albert
Albert
Albert
Albert
Barbara-Anne
Ammy
Zain
Dianne
Fransi
Morne
Lulu
Tsjaka
Allan
Allan
Luke
Allan
Allan
Allan
Allan
Luke
Luke
Luke
Reina
Monica
Mariette
Marjean
Hennie
Chris
Mark
George
Sebastian
Duncan
Mientie
1989
1991
1992
1993
Mean
1988
1991
1993
1994
1996
Mean
1996
1990
1991
1992
1993
1994
Mean
1990
1993
1994
2000
1992
1995
1996
1998
1999
2001
2002
Mean
0.654
0.525
0.472
0.464
0.53
0.323
0.459
0.566
0.601
0.458
0.48
0.430
0.525
0.496
0.493
0.587
0.269
0.47
0.340
0.352
0.324
0.361
0.389
0.335
0.331
0.398
0.382
0.299
0.357
0.35
0.384
0.36
0.382
0.348
0.37
0.318
0.222
0.474
0.338
0.216
0.31
0.345
0.366
0.308
0.621
0.538
0.341
0.42
0.294
0.325
0.266
0.538
0.512
0.278
0.414
0.369
0.322
0.203
0.411
0.36
Parsons et al.: Genetic index for stripe-pattern reduction in the zebra
115
Appendix B (continued )
Sire
Dam
Offspring
Y.O.B.
Region 1
Region 3
Sokkies
Alex
Alex
Alex
Alex
Alex
Jeanetta
Simone
Emse
David
Eric
Melanie
Alex
Alex
Alex
Alex
Alex
Alex
Alex
Alex
Alex
Alex
Alex
Alex
Libby
Nicola
Amanda
Shaun
Luke
Leius
Deirdre
Lois
Lexus
Manie
Koos
Himma
Charlene
Megavolt
Alex
Megavolt
Megavolt
Alex
Alex
Megavolt
Megavolt
Ziggi
Ziggi
Ziggi
Marilyn
Allmi
Ginny
Vernon
Quahashi
Storm
Kwaze
Vuyo
Lawrence
Marilyn
Ike
Ike
Ike
Stelza
Audri
Emilene
Monica
Shaun
Shaun
Shaun
Allan
George
Ryan
Leslie
Elizabeth
Matz
Thor
Jeanetta
Shaun
Shaun
Shaun
Shaun
George
Louis
Lindsay
Caroline
Whity
Tim
Try-Me
Albert
Paul
Paul
Albert
Paul
Paul
Denise
Mandy
Erna
Andreas
Klaus
Connie
1993
1994
1991
1995
1996
Mean
1993
2000
2001
1992
1995
1999
1996
1997
1999
2002
2003
2004
Mean
1999
1993
2000
2002
1991
1994
1996
1998
2003
2004
Mean
1999
2001
2004
Mean
1999
2000
2001
1997
2003
Mean
1997
1999
1998
2002
2003
Mean
1997
1999
2000
1996
2002
2003
Mean
0.277
0.326
0.454
0.459
0.393
0.3818
0.599
0.476
0.306
0.476
0.431
0.407
0.590
0.624
0.397
0.575
0.226
0.476
0.47
0.421
0.432
0.514
0.482
0.527
0.3
0.5246
0.482
0.286
0.375
0.43
0.367
0.416
0.367
0.38
0.406
0.425
0.51
0.35
0.54
0.45
0.331
0.42
0.628
0.423
0.563
0.47
0.603
0.412
0.400
0.617
0.452
0.531
0.50
0.252
0.245
0.288
0.293
0.154
0.246
0.509
0.36
0.452
0.403
0.439
0.53
0.302
0.363
0.499
0.503
0.31
0.355
0.42
0.524
0.349
0.508
0.537
0.339
0.265
0.29
0.397
0.329
0.328
0.39
0.402
0.37
0.398
0.39
0.521
0.289
0.385
0.144
0.613
0.39
0.398
0.346
0.35
0.404
0.581
0.42
0.574
0.381
0.502
0.362
0.503
0.533
0.48
116
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
Ricky
Fanie
Fanie
Fanie
Fanie
Fanie
Fanie
Fanie
Leon
Lindsay
Leon
Erica
Canya
Stephan
Teib
Deon
Ralph
Mathews
Benni
Howey
Alex
Alex
Alex
Bernard
Erina
Hans
Mariette
Luke
Megavolt
Luke
Luke
Luke
Cedric
Zephyr
Tracy
Joy
EricH
Marcelle
?
Ike
Ike
Ike
Ike
Ike
Genis
Karl
Jaunie
Rene
Susan
Anine
Theresa
Hennie
Hennie
Mike
Etienne
Lal
Fritz
Zephyr
Luke
Luke
Truida
Butch
Rene
Shaun
Ike
Shaun
Johan
Linda
Griet
Susan
Shaun
Shaun
Nina
Rosa
Tracy
Etienne
Etienne
Douw
Frank
Amanda
Luke
Luke
Gary
Robin
Celeste
Albert
Albert
Cima
Medee
1998
2000
1996
1997
1999
2001
2002
2004
2005
Mean
1999
2000
2002
Mean
2001
1997
2000
2002
2003
Mean
1996
2002
2003
1998
1999
2001
Mean
1998
2000
2002
Mean
2002
2003
Mean
2003
2001
2004
Mean
2003
2004
Mean
2003
2004
Mean
2003
2004
Mean
1995
1998
Mean
0.307
0.299
0.525
0.331
0.286
0.456
0.482
0.497
0.379
0.40
0.493
0.414
0.444
0.45
0.492
0.458
0.394
0.25
0.496
0.42
0.318
0.32
0.38
0.38
0.398
0.093
0.31
0.363
0.402
0.454
0.41
0.418
0.366
0.39
0.394
0.309
0.41
0.37
0.445
0.393
0.42
0.33
0.283
0.31
0.339
0.373
0.36
0.5
0.415
0.46
0.421
0.245
0.231
0.337
0.319
0.411
0.391
0.394
0.408
0.35
0.5
0.477
0.373
0.45
0.288
0.416
0.378
0.215
0.583
0.38
0.279
0.449
0.307
0.203
0.289
0.254
0.30
0.316
0.433
0.668
0.47
0.498
0.459
0.48
0.313
0.292
0.277
0.29
0.229
0.389
0.31
0.239
0.381
0.31
0.234
0.424
0.33
0.545
0.56
0.55