Different hunting strategies select for different weights in red deer

Abstract

Much insight can be derived from records of shot animals. Most researchers using such data assume that their data represents a random sample of a particular demographic class. However, hunters typically select a non-random subset of the population and hunting is, therefore, not a random process. Here, with red deer (Cervus elaphus) hunting data from a ranch in Toledo, Spain, we demonstrate that data collection methods have a significant influence upon the apparent relationship between age and weight. We argue that a failure to correct for such methodological bias may have significant consequences for the interpretation of analyses involving weight or correlated traits such as breeding success, and urge researchers to explore methods to identify and correct for such bias in their data.

Full text

Biol. Lett. (2005) 1, 353–356
doi:10.1098/rsbl.2005.0330
Published online 17 June 2005
Different hunting strategies
select for different weights
in red deer
Marı
´
a Martı
´
nez
1,
*
, Carlos Rodrı
´
guez-Vigal
2
,
Owen R. Jones
3
, Tim Coulson
3
and Alfonso San Miguel
1
1
E.T.S. Ingenieros de Montes, Ciudad Universitaria s/n,
28040 Madrid, Spain
2
Centro “Quintos de Mora”, Organismo Auto
´
nomo Parques Nacionales,
c/ Costanilla de San La
´
zaro no. 3, 45003 Toledo, Spain
3
NERC Centre for Population Biology, Imperial College London,
Silwood Park Campus, Ascot, Berks SL5 7PY, UK
*Author for correspondence (martinezmari@gmail.com)
Much insight can be derived from records of
shot animals. Most researchers using such data
assume that their data represents a random
sample of a particular demographic class. How-
ever, hunters typically select a non-random
subset of the population and hunting is, there-
fore, not a random process. Here, with red deer
(Cervus elaphus) hunting data from a ranch in
Toledo, Spain, we demonstrate that data collec-
tion methods have a significant influence upon
the apparent relationship between age and
weight. We argue that a failure to correct for
such methodological bias may have significant
consequences for the interpretation of analyses
involving weight or correlated traits such as
breeding success, and urge researchers to
explore methods to identify and correct for such
bias in their data.
Keywords: Cervus elaphus; trophy-stalking; monterı
´
a;
management; bycatch; selection
1. INTRODUCTION
Considerable ecological and evolutionary insight may
be derived from records of shot animals (Mysterud
et al. 2001; Yoccoz et al. 2002; Bonenfant et al. 2003;
Carranza et al. 2004). Although this approach often
controls for age and sex, it is typically assumed that
shot individuals represent a random sample of the
particular demographic class to which the animals
belong. However, hunting is often not a random
process with hunters often selecting a non-random
subset of the population (Noss 1999). A failure to
understand and correct for bias introduced in this
way may have important consequences for the correct
interpretation of analyses of hunting data.
It is perhaps surprising that such corrections,
although routinely carried out by fisheries managers
(Murphy & Willis 1996), are rarely used in terrestrial
population biology. This is despite the fact that
many researchers have highlighted the influence that
selective hunting may have on the population dynamics
of the system they are studying (Ginsberg & Milner-
Gulland 1994; Laurian et al. 2000) and are, thus,
aware of the bias that exists in data sourced from hunts.
Given that red deer are often hunted for commer-
cial purposes (as a trophy or for meat), or for
conservation and management purposes, an import-
ant source of bias may be the objective of the hunt
and the resulting selection for different characteristics.
For example, in a commercial hunt, there may be
selection for large antler or body size whereas in a
management hunt the deer may be selected for small
size or disease status. The introduction of such bias is
likely not only to affect the apparent age, size or sex
structure of the population but may also influence the
apparent statistics of other traits such as antler size,
body mass and disease status. The particular method-
ology used by the hunter, which varies across
cultures, may also influence apparent population
characteristics. Examples of such contrasting method-
ologies include solitary stalking, hunting in groups
and hunting with or without dogs. It is likely that the
efficiency (in terms of number killed) and selective
bias of such hunts vary widely.
In this paper we use a dataset collected from ‘Los
Quintos de Mora’, a ranch located in Toledo, Spain,
to study the effect that one important source of bias,
hunting type, has on the apparent functional relation-
ship between age and weight in male red deer (Cervus
elaphus). This unusual dataset is ideal for such a task
because, unlike most hunting datasets, the method-
ology of the hunt is recorded along with sex, age and
weight information. We demonstrate that, for this
Mediterranean population, the method of hunting has
a significant influence on the age–weight relationship
and argue that a failure to correct for the bias
introduced would produce spurious results in analyses
made using the data.
2. MATERIAL AND METHODS
(a) The dataset and study area
The data used in this study were collected in Los Quintos de Mora,
a 6864 ha fenced ranch located in Toledo, Spain and managed by
OAPN (Organismo Auto
´
nomo de Parques Nacionales). The main
human activity in this Mediterranean ranch is hunting, which
occurs between September and February.
Here we use sixteen years (1988–2003) of data on the hunting
of male red deer. This dataset includes information on the hunting
method and the dates on which the animals were killed. The weight
of the whole animal (to the nearest kilogram) and its age (estimated
using two methods: by assessing tooth wear and by counting tooth
rings) were also recorded.
A number of hunting types are used in the ranch, depending on
the management objectives. We consider four of these hunting
types: (i) bycatch is defined as non-target red deer caught by
hunting dogs during hunting; (ii) monterı
´
a is when deer are driven
towards concealed hunters by dogs and dog handlers so that the
hunters can attempt to shoot the best deer; (iii) trophy-stalking
consists of a hunter choosing a target male because of its trophy
value and then stalking it until he considers that he has an accurate
shot with which to kill the deer; and (iv) management hunting is
when hunters attempt to target ‘poor quality’ males (who may be
diseased, smaller than average, etc.). This hunting type is used to
reduce the deer population to carrying capacity within the ranch
and to increase antler quality.
In age estimation, for the tooth ring method, the left I1 incisors
were analysed by counting cementum growth rings either in
Matson’s Laboratory (USA) or by staff at the Los Quintos de Mora
laboratory. This method is an accurate estimator of age (Grue &
Jensen 1979). The tooth wear method is based on a predictable
pattern of tooth exchange and wear that occurs in the deer. We
followed the methods of Lowe (1967) with some modifications
because the tooth infundibulum is not totally isolated in deer from
Los Quintos de Mora until 10 years of age, instead of the eight
years for the deer on Rum.
Received 10 December 2004
Accepted 1 April 2005
353 q 2005 The Royal Society

(b) Statistical methods
A nonlinear least-squares regression model, using male data
collected across all years (1988–2003), was constructed in order to
characterize the weight of the hunted population, and to determine
the effects of age (fitted as a continuous variable) and hunting type
on weight.
To describe how weight changes as a function of age for each
hunting type we fitted a family of asymptotic exponential curves to
the data by obtaining least-squares estimates of the parameters a, b
and c for each hunting type (equation (2.1)):
weight Z a K b

e
ðKc

AgeÞ
jhunting type: (2.1)
However, there are no data for young animals due to the nature of
the monterı
´
a and trophy-stalking hunting types. Therefore, the
curves were all forced to pass through the overall average weight of
calves and, thus, a condition of the model was that weight
(ageZ0)
Z
30.37ZaKb.
Before analysis it was necessary to correct weight in order to
account for the confounding effects of the month of the hunt
(figure 1). We did this by adjusting the weights to what would be
expected in November using a fully specified generalized linear
model to model weight as a function of age and month, using a
subset of 1145 males killed using management hunting (r
2
Z0.831).
We also corrected age to account for methodological differences
in the age estimation techniques. To achieve a single estimate of
age, age used in these analyses was defined as the tooth ring
age (TR
age
) where available, and was predicted from tooth
wear age ( TW
age
) using a regression model when only TW
age
was measured. The model used was: TR
age
Z0.945 ! TW
age
(r
2
Z0.963, with a subset of 462 male and female individuals where
age estimates were available from both methods).
3. RESULTS
The nonlinear regression model explaining weight as
a function of age and hunting type showed that there
were significant differences in the weight–age relation-
ships for animals killed using different hunting types
(figure 2). The parameter estimates for the model,
including the standard errors, are given in table 1.
These estimates are significantly different from
each other when the 95% confidence limits of the
estimates do not overlap. Using a pairwise compari-
son approach, we show that the parameter estimates
for the four hunting types are significantly different
in all cases except for the comparison for parameter b
between the management and bycatch hunting types.
Thus, the difference between the asymptote and
the intercept (b; in most cases), and the slopes of the
curve (c; in all the cases) are significantly different.
4. DISCUSSION
Bias is widespread in most ecological data, especially
those collected by harvesting or hunting which are, by
nature, selective (Ginsberg & Milner-Gulland 1994;
Noss 1999; Laurian et al. 2000). Although introduced
bias is routinely corrected for in fisheries systems
(Murphy & Willis 1996), similar bias is often ignored
in terrestrial systems. The present study, therefore,
seeks to highlight this issue by using a hunting-
derived dataset to examine the bias introduced by
particular hunting methodologies.
0 5 10 15
150
100
50
0
200
a
g
e (
y
rs)
weight (kg)
montería
trophy-stalking
management
bycatch
Figure 2. The relationship between age and weight for each
hunting type considered in this study. The points represent
empirical data while the lines show predictions for each
hunting type from the nonlinear regression model (see table
1 for details). The ranges for which data are available are
depicted with heavy lines, while areas for which we have no
empirical data are shown with faint grey lines.
Sept Oct Nov Dec Jan Feb
huntin
g
month
count
50
100
0
150
200
250
300
350
bycatch
monter
í
a
trophy-stalking
management hunting
Figure 1. Number of animals shot between September and February (1988–2003) using each hunting type.
354 M. Martı
´
nez and others Weight selection in hunting
Biol. Lett. (2005)

Our results demonstrate that the hunting type used
to collect the data has a significant influence upon the
apparent relationship between weight and age. This
bias is most pronounced (i.e. the differences between
predicted weights for different hunting types are
greatest) in young or adult deer rather than in very
old deer. It is clear, therefore, that a hunter’s objective
has a significant effect on the characteristics of the
shot animals and on selection pressure since larger
animals are more likely to be shot at a younger age.
The objective of trophy-stalkers is to obtain the
best (i.e. largest) trophy. Therefore, only adult animals
(older than four years) are shot and there appears to
be a threshold weight so that the weight of animals
shot using this method changes little between four and
twelve years of age. On the other hand, although
monterı
´
a hunters have the same aims as trophy-
stalkers, they are apparently not as good at selecting
for weight. This is probably because they are typically
confronted with a larger number of animals that they
can shoot but have less time to make an assessment
and selection. Therefore, the animals they select are
not as heavy as those shot using trophy hunting at
young ages, and some young (less than 4 years) and
small animals are shot, presumably in order to fill
their quota. Finally, animals of every age are selected
with bycatch and management hunting methods and,
therefore, a more complete population sample may be
possible. However, since both bycatch and manage-
ment hunting tend to eliminate low-quality deer for
every age, the sample is certainly not random. These
selection pressures have clear consequences for wild-
life management (Coltman et al. 2003).
When interpreting data collected by such an
invasive method as hunting it is worth considering
that simply the collection of the data can influence
the system. In natural systems there is selection
pressure to be large/heavy to ensure breeding success
and survival (Saether 1997). Furthermore, in many
systems anthropogenic hunting represents an
additional source of selective pressure (Ginsberg &
Milner-Gulland 1994; Laurian et al.2000). The
direction and strength of these pressures depend on
the hunting methodology. For example, when man-
agement hunters shoot the weak/small individuals
there may be a large positive selection on body mass.
A more common situation in private Spanish ranches
managed for profit, is that commercial hunting is
more common than management hunting of males. It
is clear that the balance of these natural and anthro-
pogenic selection pressures may influence both the
demographic structure and the dynamics of the
system and should therefore be taken into account
when comparing systems in different locations with
differing selection pressures.
Although recent studies have shown that senescence
occurs in red deer males above 10 or 11 years old
(Mysterud et al. 2001; Carranza et al. 2004), we did
not consider it in our analyses because 95.7% of the
animals were under 11 years old. It is also pertinent to
note that data were scarce for very young ages and,
therefore, care should be taken not to over-interpret
the model within these areas of parameter space.
Nevertheless, our results show that the apparent
functional relationship between age and weight
depends upon the methodology used to collect the
data. Weight and body size are typically highly
correlated and have a huge influence on individual
fitness (Saether 1997). Clearly, bias introduced by a
failure to control for hunting methods in the esti-
mation of weight-related effects may have significant
consequences for the interpretation of analyses invol-
ving weight or correlated traits such as breeding
success (Clutton-Brock et al. 1982; Langvatn et al.
1996; Yoccoz et al. 2002; Bonenfant et al. 2003). We
therefore urge researchers to explore methods to
identify and correct for such bias in their data. We
realize, however, that such bias may not be constant
through time or space and, therefore, that correction
over such scales may not be straightforward.
We thank all the staff at Los Quintos de Mora and Mick
Crawley for assistance with this work. M.M. was funded by
a grant from U.P.M. while carrying out this work.
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Table 1. Parameter estimates for the nonlinear regression model explaining weight as a function of age.
(The coefficients are those that appear in equation (2.1) and describe the shape of the weight–age relationship curves. The
four hunting types are described in §2. The model explains most of the observed variation in weight (nonlinear r
2
Z0.830).
Residual standard errorZ11.82 on 1260 d.f.)
coefficient hunting type estimate s.e.m. t-value p-value
b bycatch 74.53 4.61 16.17 !0.001
monterı
´
a 98.61 9.55 10.32 !0.001
trophy-stalking 86.56 2.29 37.76 !0.001
management hunting 77.89 1.11 70.00 !0.001
c bycatch 0.29 0.05 5.90 !0.001
monterı
´
a 0.25 0.06 3.89 !0.001
trophy-stalking 0.65 0.23 2.75 0.172
management hunting 0.39 0.01 29.63 !0.001
Weight selection in hunting M. Martı
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nez and others 355
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Biol. Lett. (2005)