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© WILDLIFE BIOLOGY · 9:3 (2003)
Until recently, the saiga antelope Saiga tatarica was an
abundant and commercially important hunted species
of the semi-arid rangelands of Central Asia (Bekenov,
Grachev & Milner-Gulland 1998, Sokolov & Zhirnov
1998). However, it was heavily poached following the
break-up of the Soviet Union, with hunters particular-
ly targeting males for their horns, which are used in tra-
ditional Chinese medicine. The saiga was reassigned
A comparison of age estimation methods for the saiga antelope
Saiga tatarica
Monica Lundervold, Rolf Langvatn & E.J. Milner-Gulland
Lundervold, M., Langvatn, R. & Milner-Gulland, E.J. 2003: A comparison of
age estimation methods for the saiga antelope Saiga tatarica. - Wildl. Biol. 9:
219-227.
Age estimation is particularly crucial for the conservation of the saiga antelope
Saiga tatarica, but modern laboratory methods for aging have not previously
been applied to this species. There is an urgent need for evaluation of the tech-
niques that could be used for age estimation in order that long-term ecologi-
cal data sets can be correctly interpreted and conservation advice given. We eval-
uated the repeatability, practical feasibility and comparability of three techniques
for age estimation of saiga antelopes; the tooth sectioning technique (TS), the
tooth eruption and wear technique (TEW), and a visual aging technique rou-
tinely used in field studies. We found that TS and TEW gave repeatable results,
and agreed well. The visual method underestimated the age of males compared
to laboratory methods. It assigned animals consistently to the age class of at
least one year old, but less consistently to the age class less than one year old.
Although studies of known-age animals are needed to evaluate precision and
accuracy of these methods, we suggest that either TS or TEW would be suit-
able for aging saiga antelopes, with the choice being determined by practicalities
such as the availability of the necessary expertise and equipment.
Key words: aging, Kazakhstan, saiga, tooth eruption, tooth sectioning, tooth
wear, ungulates
Monica Lundervold, Department of Biological Sciences, University of Warwick,
Coventry CV4 7AL, UK - e-mail: monica@tinyworld.co.uk
Rolf Langvatn, Foundation for Nature Research (NINA), Tungasletta 2, N-7485
Trondheim, Norway - e-mail: rolf.langvatn@unis.no
E.J. Milner-Gulland, Department of Environmental Science and Technology,
Imperial College London, South Kensington Campus, London SW7 2AZ, UK
- e-mail: e.j.milner-gulland@imperial.ac.uk
Corresponding author: E.J. Milner-Gulland
Received 21 October 2002, accepted 28 January 2003
Associate Editor: Johan T. du Toit
from Near-Threatened to Critically Endangered in the
2002 Red List of threatened plants and animals com-
piled by IUCN - the World Conservation Union (http://
www.redlist.org/, see Hilton-Taylor 2000), because
poaching has led to a ~90% reduction in the population
size over the last 10 years (Milner-Gulland, Kholodova,
Bekenov, Bukreeva, Grachev, Amgalan & Lushchekina
2001).
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Age determination is a key component of ecological
studies of wild animals and rigorous methods for aging
saigas are urgently required. In particular, there is con-
cern that reproductive collapse is occurring, with reports
of the majority of one-year old females in the Kalmykian
population failing to conceive apparently due to a lack
of adult males (Milner-Gulland, Bukreeva, Coulson, Lu-
shchekina, Kholodova, Bekenov & Grachev 2003).
There is also concern about the vulnerability of saiga
populations to epidemic disease transmitted from live-
stock, because of the collapse in veterinary services in
Kazakhstan (Lundervold 2001). The age of individu-
als needs to be estimated for information on disease dy-
namics to be obtained from age-seroprevalence profiles.
The age profile of the population and of poached indi-
viduals can give an indication of hunting selectivity and
population structure (Fadeev & Sludskiy 1982).
A wealth of detailed long-term data on saiga ecolo-
gy is available. For example, saiga pregnancy rates by
age class were used in a comparative study of the effects
of density dependent and independent factors on ungu-
late population dynamics (Coulson, Milner-Gulland &
Clutton-Brock 2000). Long-term field studies over
decades have produced published data sets on age class-
specific fecundity rates and population structure (Banni-
kov, Zhirnov, Lebedeva & Fandeev 1961, Fadeev & Slud-
skiy 1982, Bekenov et al. 1998, Sokolov & Zhirnov
1998). These studies provide a valuable foundation for
understanding the ecological factors involved in the
saiga’s current precarious position, and for making
recommendations for the most effective forms of con-
servation action. The studies rely on visual estimation
of age (using body size, condition and male horn char-
acteristics), and if saigas are killed, a visual inspection
of tooth eruption and wear also takes place (see below).
The ability of these methods to provide good age esti-
mates has not previously been tested, however; hence
the reliability of the age data in these long-term data sets
is unknown.
It is currently illegal to kill saigas in Kazakhstan,
even under scientific licence. There are a few known-
age animals in captivity in Kalmykia (Russian Federa-
tion) and in zoos, but the precarious status of the spe-
cies means that culling these individuals for scientific
purposes is unjustifiable. We carried out a study of the
ecology and epidemiology of the saiga antelope in
1996-1997, before the catastrophic decline in popula-
tion size. The study involved sampling a large number
of individuals culled by commercial hunters (see Lunder-
vold 2001 for details). Here we report on our use of the
samples for an assessment of three methods for esti-
mating saiga age; tooth sectioning, tooth eruption and
wear, and the visual assessment technique that is cur-
rently used by saiga researchers.
Methods for age estimation in ungulates
Among the techniques to estimate age in mammals
(see Morris 1972 for a review), dentition (tooth erup-
tion pattern, tooth wear and tooth sectioning) is partic-
ularly useful in ungulates (Klevezal & Kleinenberg
1967, Grue & Jensen 1979, Jacobson & Reiner 1989,
Brown & Chapman 1991c, Langvatn & Meisingset
2001). Tooth eruption is accurate for aging sheep from
the ages of 15 months (when the first, most central, per-
manent incisors erupt) to 33 months (when the fourth,
most lateral, permanent incisors erupt; Williams 1988).
In many wild ungulate species (e.g. Javan rusa deer Cer-
vus timorensis russa, red deer Cervus elaphus, fallow
deer Dama dama and roe deer Capreolus capreolus),
eruption of permanent teeth is complete by 18-36 months,
so aging beyond this limit is more difficult (Brown &
Chapman 1991b, Brown & Chapman 1991a, Ratcliffe
& Mayle 1992, Moore, Cahill, Kelly & Hayden 1995,
Bianchi, Hurlin, Lebel & Chardonnet 1997, Langvatn
& Meisingset 2001).
Tooth wear methods, usually based on the extent of wear
of mandibular teeth, have been used for age estimation
in moose Alces alces and roe deer (Passmore, Peterson
& Cringan 1955, Brown & Chapman 1991c). However,
the accuracy is poor (Hewison, Andersen, Gaillard,
Linnell & Delorme 1999). Kierdorf & Becher (1997)
showed that using enamel hardness to modify wear
indices improved the accuracy of age estimation. In both
moose and reindeer Rangifer tarandus, tooth section-
ing (TS) has given more accurate results than tooth-wear
(Sergeant & Pimlott 1959, Reimers & Nordby 1968,
Grue & Jensen 1979). TS was pioneered by Laws
(1952) for seals, and involves preparing thin sections of
the canine teeth and examining the regular sequence of
growth layers (annuli) in the dentine. Sergeant & Pimlott
(1959) used TS to age moose. They suggested a seasonal
sequence of deposition of the annuli, and considered
accuracy to be plus or minus one year for younger ani-
mals, and two years for older animals. They therefore put
animals into age classes, e.g. 5-6 years, 6-8 years, rather
than attempting accurately to estimate their actual age.
According to Ratcliffe & Mayle (1992), precise assess-
ment of the age of adult ungulates is most reliable
using TS. For example, roe deer can be aged to the near-
est month if the collection date is known (Aitken 1975,
Ratcliffe & Mayle 1992). However, Moore et al. (1995)
compared different techniques for age estimation of fal-
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low deer, and found that incisor height, molar height,
molar wear and annuli in dental cementum were direct-
ly comparable techniques. Of these four methods, in-
cisor height was most appropriate for the study popu-
lation, accurately aging almost 90% of males.
Methods for age estimation in saigas
The primary reference for saiga age estimation is a
detailed key by Bannikov, Zhirnov, Lebedeva & Fandeev
(1961). However, no explanation is provided of the
methods used to develop the key, and in particular there
is no reference in that or later publications to the use of
known-age animals. Hence the relationship between
saiga age and the patterns of eruption and wear given
in the key remains unclear, as does the variability in these
patterns between individuals and locations.
For both sexes Bannikov et al. (1961) used tooth erup-
tion up to the age of 19 months, and above 19 months
they advocated tooth wear methods, using the molars.
In this way, they grouped animals into age categories
of 3, 4, 5-6, 7-8, and 9-10 years. Non-intrusive age esti-
mation is suggested to be possible for male saigas ≤1.5
years old, using horn size and shape. After the age of 1.5
years, horns are thought only to be capable of giving a
rough estimate. Young horns are shorter and straighter
with black tips; the older the animal gets the more
lyrate the horns become and the tips become less black.
(Bannikov et al. 1961, Sokolov & Zhirnov 1998). For
females, visual aging is more difficult, but intrusive
methods can distinguish juvenile animals (≤1 year old)
from other age classes because the last pair of molars
has not yet erupted (Bekenov et al. 1998).
Aging is made easier by the saiga’s life cycle, which
is strongly influenced by the highly seasonal and harsh
climate in which the species lives. Births take place over
a short period (about 10 days) in May, hence animals
can be reliably placed into non-overlapping age classes.
This strong seasonality suggests that pronounced annuli
in the tooth cementum are likely to be present. However,
Pronyaev, Fandeev & Gruzdev (1998) used the tooth sec-
tioning technique (TS) and found that the annuli were
often not precisely defined. In their study, only 24% of
incisors showed clearly visible annuli (N = 68). A fur-
ther 33% were good enough for analysis, 37% were ana-
lysed with difficulty, and in 6% the annuli were not dif-
ferentiable. Therefore, they recommended that Bannikov
et al.’s (1961) technique should be used until further re-
search had been carried out on TS.
Material and methods
We collected data from saiga antelopes culled in the
course of a wider ecological survey (see Lundervold 2001
for details). Animals were shot under permit either by
professional hunters or by scientific personnel of the In-
stitute of Zoology of the Kazakhstan Academy of Scien-
ces. Sampling took place during the hunting seasons in
November-December 1996 and 1997 (117 males and 239
females), with a further eight males sampled in May
1997. As all saigas are born in May, animals culled in
November are 0.5 years, 1.5 years, 2.5 years old, and
so forth. To provide supplementary information on sex-
and age-specific growth, the anterior and posterior parts
of the mandible and the total mandible length were
measured in specimens with complete lower mandibles
(N = 57), following Langvatn (1977).
All animals were aged using visual assessment. Age
estimates for specimens with mandibles were initially
based on TEW, following the criteria described by Ban-
nikov et al. (1961). Two incisors were then removed for
TS. Another 180 animals for which only incisors were
available resulted in a total sample size of 237 for TS.
There were no known-age animals in the sample.
Age estimation by the tooth sectioning technique
(TS)
The first incisor (I
1
) is the largest and the first to erupt,
at the age of 13-14 months (Bannikov et al. 1961). It was
therefore chosen for age estimation. In young animals,
the first incisor of the milk teeth (dI
1
) was used. Incisor
roots are easier to process than roots from molars, and
they usually have a well defined dental cementum lay-
er (Reimers & Nordby 1968, Grue & Jensen 1979). First
incisors were available from 180 saigas. We also removed
a further 57 I
1
incisor pairs from the saiga mandibles that
had been used for the TEW analysis. Microscope slides
were produced from decalcified, sagital sections of the
incisor roots, following Reimers & Nordby (1968). An-
nuli in the dental cementum were counted and the age
was estimated based on the time of eruption of the per-
manent first incisor (Bannikov et al. 1961, Gruzdev &
Pronyaev 1994). The procedure was repeated using a
different section from the same tooth, providing two age
estimates for each individual.
Age estimation by analysis of tooth eruption and
tooth wear (TEW)
Age estimation by TEW was performed on 57 mandi-
bles. We scored eruption and development patterns in
specific teeth using a four-stage assessment. Incisors (I
3
)
and canines (C) are the last permanent teeth, erupting
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at the age of 19-26 months (Bannikov et al. 1961). This
enabled us to allocate individuals to three age classes,
rather than the two that can be distinguished based on
molars alone (M
3
is fully developed at approximately
12-14 months). We classified individuals as calves
(approximately 0.5-1 years old), subadults (approxi-
mately 1-2 years old) or adults (fully developed denti-
tion with permanent teeth). Considering the timing of
the birth season and culling, young animals could then
be tentatively assigned an age in months (see Bannikov
et al. 1961). This presumption is further supported be-
cause narrow, dark annuli are deposited in late winter
or spring, whereas broader, light cementum zones are
grown in summer and autumn (Grue & Jensen 1979,
Langvatn 1995). However, variation in the progress of
tooth eruption in some cervids (Langvatn & Meisingset
2001), suggest that age classifications of young animals
should be interpreted with caution.
We estimated the age of individuals with fully develop-
ed permanent dentition from the degree of wear, pri-
marily on premolars and molars. Crown height, oclusal
surface, and the shape of crests and cusps on specific
teeth were used as criteria, combined with general ex-
perience on lifespan-wear patterns in other bovids and
cervids. We did not attempt to sex individuals based on
tooth characteristics, although in some cervids male teeth
wear faster than those of females (Grue & Jensen 1979,
Peterson, Schwartz & Ballard 1983). Based on TS,
adult cervid males can sometimes be distinguished
from females by narrow dark lines between annuli in the
cementum. Such lines may be related to rutting activ-
ity (Grue & Jensen 1979, Bartos, Malik, Hyanek, Vavru-
nek & Bytesnik 1984). No information about individ-
ual samples (e.g. sex, body condition and date of sam-
pling) was available prior to analysis. TEW was repeat-
ed twice without cross-referencing, providing two inde-
pendent age estimates.
Age estimation by visual assessment
Visual estimations of age were made by A. Grachev, a
technician from the Institute of Zoology, Kazakhstan,
with many years’experience working with saigas, using
the eruption of the last molars and the general appear-
ance of the animal as age criteria. The assessment was
carried out on whole individuals in the field, rather
than on mandibles as used for the TEW method. He dis-
tinguished 0.5 year-old females from those ≥1.5 years
old based on whether the last molars were present or not.
Males were aged using horn shape and size. This esti-
mation technique is the one used routinely in previous
and current studies of saiga ecology, hence it is impor-
tant to compare it to the less subjective TS and TEW
methods.
Results
Repeatability of the laboratory methods
Assigned ages ranged from 0.5 to 10.5 years (TEW) or
11.5 years (TS) for females, and from 0.5 to 7 (TEW)
or 5 (TS) years for males. Consistency between the two
iterations was high for both methods, but in both cases
discrepancies were more likely to occur in older animals
(Table 1). Previous studies have found that accurate age
estimation by TEW is more difficult in older than in
younger animals (Jacobson & Reiner 1989, Ratcliffe &
Mayle 1992, Bianchi et al. 1997). In our study the ani-
mals with assigned ages that varied by two years using
TEW were estimated to be 3-5 years old or older. TS
gave estimated ages three years apart in three cases; the
first was a mistake, the other two were in older animals,
aged 4.5 or 7.5 years, and 8.5 or 11.5 years, respectively.
The same animals tend to be problematic for both
methods (P
2
= 17.1, df = 1, P < 0.001). These results are
not surprising, as it would be expected that the level of
discrepancy between the results might be greater for old-
er animals, for which a broader range of potential ages
Table 1. Repeatability of the tooth eruption and wear technique (TEWT) and tooth sectioning technique (TST), and comparability between
the mean ages assigned by the two techniques (TST vs TEWT). For the comparability test, differences of 0.5 and 1 are shown in the 1-year
difference row, differences of 1.5 and 2 in the 2-year difference row. Columns show the number of samples (N) with a given difference between
their assigned ages, the percentage of the total sample that N represents (%), and the mean age of animals with a given difference (Age). In
all cases, the mean estimated age increases significantly with the amount of discrepancy between the two tests (ANOVA tests: TST - F =
12.5, df = 235, P < 0.001; TEWT - F = 38.1, df = 56, P < 0.001; TST vs TEWT - F = 32.5, df = 56, P < 0.001), however, when the young
animals for which discrepancies do not occur are removed, the evidence for an increase in the discrepancy with age is less strong (ANOVA
tests: TST - F = 5.62, df = 188, P = 0.001; TEWT - F = 3.46, df = 23, P = 0.05; TST vs TEWT - F = 0.97, df = 23, NS).
TEWT TST TST vs TEWT
Difference N % Age N % Age N % Age
None 38 67 1.5 145 61 2.1 39 68 1.5
1 year 10 18 4.3 78 33 3.2 15 26 5.0
2 years 9 16 6.7 11 5 4.6 3 5 6.3
3 years 0 0 - 3 1 8.0 0 0 --
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© WILDLIFE BIOLOGY · 9:3 (2003)
is available. Mean age increases with the amount of
discrepancy between methods (see Table 1). Only ani-
mals aged ≥1 year had discrepancies between the two
iterations using the TS. For TEW, there were no dif-
ferences between iterations for animals aged ≤1.5 years.
If these younger age groups are removed, the amount
of discrepancy still increases significantly with age,
but less markedly (see Table 1). There was no evidence
that the amount of discrepancy varied by sex for either
method.
Bias between the two iterations was checked for by
linear regression. The two TS estimates were significantly
correlated (r
2
= 0.89, P < 0.001), with a very slight
tendency for the second estimate to be lower than the
first (slope = 0.95, 95% confidence interval: 0.91-0.99;
Fig. 1). The sex of the animal had no effect on this rela-
tionship. The TEW iterations were also significantly cor-
related (r
2
= 0.91, P < 0.001), with the estimated age
slightly lower the second time than the first (slope = 0.81,
95% confidence interval: 0.73-0.88). Sequential bias was
significantly related to sex (coefficients in the multiple
regression have P < 0.05 for sex and P < 0.001 for the
difference between tests). The reduction in the second
estimate was greater for males than for females (slope
for males = 0.73 and for females = 0.83; Fig. 2).
A comparison of the two laboratory methods
To compare the two methods, we first corrected for the
lower sample size available for TEW. We sampled the
TS data set randomly without replacement, to produce
1,000 subsets each of 57 TS samples (the same number
used for TEW) and 95% of the slopes obtained lay
between 0.86 and 1.04 (mean slope = 0.95; mean r
2
=
0.89). The probability that the TEW slope was drawn
from the same distribution as the TS slopes was sig-
nificantly low (P < 0.01). This confirms that the TEW
method is significantly more affected by sequential
bias than the TS method, but that the methods are very
similar in their degree of correlation between the results
of the two iterations.
We aged 57 saigas using both TS and TEW. The cor-
relation between the ages assigned by two techniques
was highly significant (r
2
= 0.97, P < 0.001, slope = 1.04;
confidence interval: 0.99-1.10; Fig. 3). The slope was
not significantly different from 1, showing that the esti-
mated age (taken as the average of the two iterations) did
not vary systematically between methods. There was no
effect of sex, but mean age was a significant source of
variation between the methods. This disappeared when
animals aged ≤1.5 years (for all of which the two
0
2
4
6
8
10
12
024681012
TST 1
TST 2
0
2
4
6
8
10
12
024681012
TEWT 1
TEWT 2
Males Females
Figure 1. Repeatability of the TS method for aging saigas. The age (in
years) assigned on the first iteration is plotted on the x-axis, with the
second iteration on the y-axis. The 1:1 line is shown in grey, regressions
as a solid black line. Only one symbol is given at coordinates where
more than one sample has the same result.
Figure 2. Repeatability of the TEW method for aging saigas. The age
(in years) assigned on the first iteration is plotted on the x-axis, with
the second iteration on the y-axis. The 1:1 line is shown in grey, regres-
sions as solid black lines. Separate lines show the regressions for
males (♦) and females (◊). Only one symbol is given at coordinates where
more than one sample has the same result.
Figure 3. Comparison of the ages (in years) assigned by two different
laboratory techniques for aging saigas; tooth eruption and wear (TEW)
and tooth sectioning (TS). The assigned age for each technique was tak-
en as the average of the two iterations. The data are shown as points,
the linear regression as a line. Only one symbol is given at coordinates
where more than one sample has the same result.
0
2
4
6
8
10
12
024681012
TST a
g
e
TEWT age
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methods gave the same results) were removed from the
analysis (see Table 1).
Comparing visual and laboratory methods
To compare the results of the laboratory aging methods
with the visual method, we consolidated the laborato-
ry age estimates into a single estimate for each animal.
The age was taken as the mean of the four TS and
TEW estimates, or as the TS estimate if TEW was not
done. Visual assessment gave an actual age for males,
but only an age class for females. Although the sample
size was small, males were consistently aged younger
by visual assessment than by laboratory analyses (Table
2).
Assignment to age classes
Since the visual assessment method is likely to perform
best at distinguishing first year animals from older ani-
mals, and because of good agreement between the labor-
atory methods in younger animals, we compared the per-
formance of all three methods at assigning individuals
to two groups: first-year and older animals. This divi-
sion is meaningful biologically and in terms of the
long-term data sets, for example because first-year fe-
males are thought to have lower fecundity rates than old-
er females (Bekenov et al. 1998).
There was much more disagreement between the
methods when animals had been visually aged as ≤1 year
than when the visual assessment was >1 year old
(Table 3). There were no differences in the age class
assigned by the two iterations of TEW, but 12% of the
TS samples were assigned differently in the two iter-
ations (differences between iterations of the TS account-
ed for 29/30 of the cases where there were inconsistencies
between the age classes assigned by laboratory meth-
ods). Hence, TEW was less repeatable than TS when actu-
al ages were assigned, but was highly repeatable when
assigning animals to age classes. There was also a high
level of agreement between the visual assessment and
TEW, with only 5% of assigned age classes differing,
as opposed to 16% difference between TS (when both
iterations agreed) and visual assessment. This agreement
between TEW and visual methods is not surprising, giv-
en that both the visual assessment and TEW use tooth
eruption to decide whether an animal is in its first year
or older. The results were similar for both sexes.
Discussion
The laboratory-based methods we used for saigas are
well-established for ungulates, and have proved suc-
cessful for other species (Grue & Jensen 1979, Ratcliffe
& Mayle 1992, Moore et al. 1995). There is a well-estab-
lished protocol for using TEW in saigas, although the
only previous study using TS for saigas was not success-
ful because the annuli were not well-defined (Pronyaev
et al. 1998). In this study we found that both TEW and
TS gave repeatable results, and that the two techniques
agreed well. Repeatability declined amongst animals esti-
mated to be older, but the degree of agreement between
the methods was not significantly related to estimated
age in animals ≥1.5 years old. Higher ages tended to be
assigned on the first iteration, which shows the impor-
tance of checking for bias by carrying out the test more
than once. TEW was significantly more affected by
bias between iterations than TS, although the degree of
Table 2. Comparison of the results of visual assessment (visual ages) and the average age (in years) assigned by laboratory methods (lab
age) for the 50 male saigas assigned ages by both methods. The cells representing agreement between the two methods are shaded grey.
Visual age
Lab age 0.511.52345Total
0.5 32 32
18 8
1.5 5 5
2 0
3 0
4 112 4
5 11
Total 37 80122050
Table 3. Comparison of the results of visual assessment and the
average age (in years) assigned by laboratory methods for the whole
sample. The animals are grouped into two age classes: Juvenile = first-
year animals, Adult = older animals. Columns represent the age class
assigned visually (visual age class). Rows indicate whether all meth-
ods agree on the age (agreed), the visual estimate differs from the
laboratory estimates (visual differs, where both iterations of both la-
boratory methods agree), or the laboratory estimates disagree (labs
disagree). Results are given as actual numbers, with the proportions
of the total number of animals assessed that these numbers represent
given in parentheses. The results differ significantly between the two
age classes (P
2
= 43.5, df = 2, P < 0.001).
Visual age class
Juvenile Adult Overall
Agreed 32 (0.43) 138 (0.85) 170 (0.72)
Visual differs 22 (0.30) 15 (0.09) 37 (0.16)
Labs disagree 20 (0.27) 10 (0.06) 30 (0.13)
Total 74 ( 163 ( 237 (
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bias was not great in either case (TEW had a 19% re-
duction in mean estimated age at the second iteration).
Males were more prone to bias in TEW estimates, per-
haps due to sex-specific differences in feeding ecolo-
gy. Although repeatability was tested only for one per-
son rather than between researchers, the two tests were
carried out blind.
Our results suggest that either technique would be use-
ful for future research in terms of repeatability, although
TS performed rather better. Logistically, TS requires labor-
atory facilities for processing teeth, microscopes to
read the annuli and trained technicians. However, the
development of a consistent TS procedure for saigas
could have major advantages for future investigation into
saiga life histories (e.g. Coy & Garshelis 1992). TEW
requires the collection and preparation of mandibles,
which may be time consuming. Mandibles are also
bulky to transport. TEW analysis is a relatively quick
procedure, but requires training for reliable and consistent
aging. Saigas aged in this study were from two popula-
tions in Kazakhstan; results might be different for popu-
lations living in other areas (where conditions influencing
tooth wear may be different).
Visual methods for age estimation are potentially
very useful, because they allow age estimation of live
animals. Despite its advantages, however, the use of visu-
al age estimation raises concerns about accuracy, partic-
ularly for older animals. Prior to our study, both horn
shape and size (for males) and eruption of posterior
molars (for both sexes) were felt to be reliable in distin-
guishing first-year animals from older animals. We
found that horn shape and size underestimated age
compared to the laboratory methods. Hence visual esti-
mation is only useful in assigning animals to two age
classes: first-year and older animals. Even then, the
time of year at which the survey is undertaken affects
the reliability and usefulness of visual methods. Our sur-
veys were undertaken during the hunting season, when
first-year animals are six months old. This is the time
at which visual age estimation is most likely to be use-
ful, because first-year animals are well-enough grown
that it is not trivial to distinguish them from older ani-
mals, but are young enough that the back molars have
not yet erupted. Distinguishing visually between one-
year-old and older females in May (when births occur)
would be more useful biologically, but may be less
reliable. Only 13 animals were aged in May during
our study (all male), all of which were placed consist-
ently into age classes 0-1 year or >1 year by all three
methods. We suggest that a full study of the reliability
of methods for aging females in the spring would be
worthwhile.
The strong agreement we found between TEW and
the visual estimate in assigning animals to age classes
confirms repeatability between researchers. This is to
be expected because both methods used molar eruptions
to age the animals (TEW used detailed analyses of clean
mandibles, visual assessment used examination of the
mouth under field conditions). We suggest that visual
estimation is consistent at placing animals in the ≥1 year
age class, when judged against laboratory methods,
but less consistent at placing animals into the 0-1 year
age class. This may be because it is easier under field
conditions to miss erupted molars (hence to underage
the animal) than it is to mistakenly see erupted molars
when there are none. The smaller sample size for TEW
means that this supposition cannot be tested, because
differences between the laboratory and visual estimates
are mostly due to TS, which does not involve looking for
molars. The use of annuli rather than threshold-based
diagnostic characters (molars) may also explain why
dividing animals into age classes rather than actual
ages did not improve the repeatability of TS, unlike TEW.
Hence if TS is used to age animals, there is no advan-
tage gained from repeatability by lumping animals into
age classes to offset the loss of information that is in-
curred.
Although we considered internal repeatability and
comparability between aging techniques for saiga ante-
lopes, we cannot make judgements about the preci-
sion or accuracy of the methods. This can only be done
with the use of known-age animals, which is clearly the
next step in developing reliable aging techniques for
saigas. Since captive herds exist in saiga breeding cen-
tres, this should be possible in the future, provided that
tooth wear patterns in captive animals are representa-
tive of patterns found in the wild. However the herds are
not large, and given the conservation status of the spe-
cies, it is not currently justifiable to kill animals for age
determination studies. Tagging studies offer opportunities
for retrospective aging of dead animals, although these
are limited by the saiga’s nomadic lifestyle. Similar pro-
cedures to those used in our study have also been
applied to other species, including known-age individ-
uals. They have proved to be satisfactory in terms of both
accuracy and precision (Clutton-Brock, Price, Albon &
Jewell 1991), suggesting that our results are likely to be
robust.
Our results suggest that the long-term data sets, which
are such a valuable resource for researchers, are robust
for the ≥1 year age class. This is the age class for which
Coulson et al. (2000) found significant effects of pop-
ulation density and climate on fecundity. However, there
is likely to be more uncertainty in the assignment of indi-
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viduals to the 0-1 year age class. This is of concern be-
cause recent suggestions of sharp declines in fecundi-
ty suggest that it is this age class which is particularly
suffering (Milner-Gulland et al. 2003). Visual assessment
of females as being in the 0-1 year age class is not
adequate for studies on which factors determine con-
ception rates, and hence how best to target conservation
action.
Acknowledgements - we gratefully acknowledge the financial
support of INTAS (projects KZ-95-29 and KZ-96-2056) and
the BBSRC. We also thank the following people for their help
and support during the project: A. Grachev, who carried out
the visual age estimations, Dr Iu.A. Grachev, who led the expe-
ditions, and Professor A. Bekenov, Director of the Institute of
Zoology, Kazakhstan. Help in data collection was also given
by Eric Morgan and by Aidar Namet of the Kazakh Scientific
Veterinary Research Institute. We thank Graham Medley for
much help and advice. We thank Anna Lushchekina, Marina
Kholodova, G. Klevezal and two referees for their helpful com-
ments on the manuscript.
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