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The importance of long-term and large-scale data sets in the evaluation of red deer management

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  • Hungarian University of Agriculture and Life Sciences (MATE) Gödöllő

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Wildlife management should be based on data and management decisions which require adequate information. In case of red deer population sizes, the level of hunting pressure, and the effects of harvesting on the male population are debated in many form. Several relationships and effects of management and hunting cannot be well understood without the collection and use of long- term data. Since 1970 it is compulsory in Hungary to present antlers of harvested red deer for a trophy evaluation (scoring) by Trophy Scoring Committees authorized by the state authorities. This paper presents some of the uses of trophy scoring data on the basis of Somogy county. Population reconstruction, comparison of mortality patterns of different cohorts, and spatio-temporal differences in age composition of shot males are discussed.
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Original scientific paper UDC: 569735
91
THE IMPORTANCE OF LONG-TERM AND LARGE-SCALE DATA SETS IN THE
EVALUATION OF RED DEER MANAGEMENT
Csányi, S.1
Summary: Wildlife management should be based on data and management decisions which require
adequate information. In case of red deer population sizes, the level of hunting pressure, and the
effects of harvesting on the male population are debated in many form. Several relationships and
effects of management and hunting cannot be well understood without the collection and use of long-
term data. Since 1970 it is compulsory in Hungary to present antlers of harvested red deer for a
trophy evaluation (scoring) by Trophy Scoring Committees authorized by the state authorities. This
paper presents some of the uses of trophy scoring data on the basis of Somogy county. Population
reconstruction, comparison of mortality patterns of different cohorts, and spatio-temporal differences
in age composition of shot males are discussed.
Key words: red deer, trophy hunting, survival curve, population reconstruction, Hungary
Introduction
Harvesting of big game populations can impose strong pressures on natural populations, and may cause
undesirable life history changes over shorter periods of time than expected from natural selection
(Coltman et al. 2003; Garel et al. 2007). The impacts of trophy hunting remains uncertain, as the current
studies mostly provide data over fairly short time spans, have low sample sizes and are not replicated time
series (Rivrud et al., 2013). As data from long-term monitoring are rare and hard to obtain in harvested
populations, patterns of harvesting selection in ungulates have been studied by comparing hunting
methods (Martinez et al. 2005; Torres-Porras et al. 2009) or categories of hunters by comparing local
hunters vs. foreign trophy hunters (Mysterud et al. 2006).
To investigate the effect of age-specific hunting mortality long-term and standardized systems of data
collection are needed. These data are essential to understand the effects of changing hunting pressures as
well as to relate their effects to human harvesting. In Hungary, since 1970 it is compulsory to present
antlers of harvested cervids and horns of mouflon rams for a trophy evaluation (Csányi and Lehoczki
2010). These data of trophy scoring are collected in a digital database since 1990. For red deer a 23 years
long data set of individually (at least partially) scored antlers are available for different investigations, e.g.
comparison of spatial differences and patterns, time series analyses of antler changes, population
reconstruction, study of hunting mortality patterns.
This paper presents some of the uses of the red deer trophy scoring data set, namely population
reconstruction, comparison of mortality patterns of different cohorts, spatio-temporal differences in age
composition of shot males.
Materials and Methods
Since 1970 it is compulsory in Hungary to present antlers of harvested red deer for a trophy evaluation
(scoring) by Trophy Scoring Committees authorized by the state authorities (Csányi and Lehoczki
2010).The trophy evaluation is done according to the International Council for Game and Wildlife
Conservations (CIC) rules of trophy measurements (CIC 2010). Antler data from >200 thousand
individuals are available for 23 unique years (1990-2012) and from all 19 counties in Hungary. The
county, date of kill and information on whether the hunter was local or a foreign trophy stalker are also
available for all individuals (Csányi et al., 2010). Age is estimated by tooth wear (Szidnai 1978), a
method known to show some variation for older individuals (Mysterud and Østbye 2006) but also
variation between (Veiberg et al. 2007) and within populations (Nussey et al. 2007). However, since the
same method is utilized over the full data set, it is unlikely that over- or underestimation of age play a role
for the observed patterns,
1 Sándor Csányi, PhD, professor, Szent István University, Faculty of Agricultural and Environmental Sciences, Institute for Wildlife
Conservation, H-2100 Gödöll, Páter utca 1. - www.vmi.info.hu – css@ns.vvt.gau.hu
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but we are aware that the ageing method probably adds some confusion to the data, particularly
for older individuals (Rivrud et al., 2013).
from these data, age distribution of harvested stags, weight distribution of antlers and the average
antler weights of each age class can be calculated. These data are published annually but for the
period of 1970-1989 only the age distribution and the weight distribution of trophies presented
for scoring are available from the annual game management statistics.
When a trophy is scored, the age of the animal holding the trophy (antlers) is estimated and these age data
are representing the 'age at death' information of each male. On the basis of these age data 1) a population
reconstruction can be done (Csányi, 2002; Csányi and Tóth, 2000) and 2) cohort life tables can be
constructed (Caughley, 1977).
In Table 1 the data for Somogy county are given for the years between 1984-2012 (an additional 4 years
is added to the table for the purpose of an extended calculation; for the data of the last age groups of
2012 are used). In the table the values of the cohort born in 1991 are shaded with yellow and the stags
older than calves are shaded with light brown. In the diagonals above the yellow one each diagonal
represent one age classes (e.g. 2, 3, 4 … 14 years, 'adults').
On the basis of these data it is possible to calculate the number of calves born between 1983-2001, as well
as the number and age distribution of the males living in the same years can be calculated. In the
following step, the mortality data can be used to construct the survival programs of each male cohorts
born between 1983-2001 (Table 2).
There are some important restrictions when we use these data:
The data represent only the males 1 year old or more since there are no information about the
annual number of male calves shot. Consequently, only a partial survival program of the males
>= 1 year can be calculated.
As only the numbers of males shot are known, the survival program reflects only the effects of
hunting-mortality. All life table parameters (survival program, mortality program or age-specific
expected life time) can only be analysed in this context.
The analysis is retrospective and depending on the life-span the results show a delayed
information and it should be carefully used for actual management decisions.
Results and Discussion
Population reconstruction on the basis of trophy scoring data: In accordance with the above methods
the male population size for 1984-2001 was calculated for Somogy county (Figure 1). Initially, the male
population size was stable around 7000 between 1984-1988. Later it declined to 5500 and then rebounded
to 7000 after 1999.
This pattern is similar to that of the reported male population (Table 3) but the calculated values of the
yearly male populations are >2 times larger than the reported male population size. In spite of the
decline of the differences, the values range between 1.5 and 1.8 for the second half of the period
investigated. These differences are important as they explain why the male population could increase in
spite of the harvest rates often being >30% (Harvest rate (1) column in Table 3). When the same harvests
are compared to the calculated male numbers, the harvest rates drop into the range of 13-22% (Harvest
rate (2)). In conclusion, these data clearly show that the male population was generally under harvested
and that after 1994 it could rapidly increase as a consequence of this under harvest (Table 3.)
These findings are in accordance with previous studies concluding that the red deer population in
Hungary was consequently under reported and at the same time under harvested (Csányi, 1991). The
hidden surplus allowed a 2-5% net annual increase of the population resulting in the virtual paradox 'the
more are shot, the more are produced' (Csányi and th, 2000). With the use of the trophy scoring data
base a more robust numerical approach can be applied as it is not so dependent of assumptions of vital
rates as another are models (Csányi, 1991).
Survival patterns of red deer males under varying shooting pressure
As it was already shown, these data of trophy scoring allows to construct cohort life tables on the basis of
the numbers of animals shot in age classes each year (Table 2.). One of the possibilities is to construct the
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hunting survival curves of each cohorts (Figure 2.) In order to compare the effects of shooting on
different cohorts specific ages or intervals can be selected.
Table 1. The basic data set for age distribution of stags shot in Somogy county (cells shaded light blue:
values assumed on the basis of 2012; cells shaded yellow: the cohort born in 1991; cells shaded light
brown: the adults living in 1991; Sum d(i,j) = cohort size based on shot males)
In Hungary, red deer males are considered 'young' between 1 and 5 years, 'medium age' between 6 and 9
years, and 'old' above 10 years. As a consequence of the increasing harvest rates the probability to survive
until 10 years decline from 25% to around 5%, to survive until 13 years declined from 4% to <0.5%. The
probability of survival until the medium age of stags also showed a decline from 60% to 30-40% (Figure
3).The changing hunting pressure increased the mortality faced by different age classes. Compared to the
initial cohorts in the young age group the hunting mortality increased from 40% to near 70%. A pattern of
mortality during the medium age class mirrors (up and down in the opposite direction) the changes of the
young age class. As a consequence that the overall mortality increased during the first 9 years of life, the
proportion of mortality during the old age declined from 20% to 5% (Figure 4).
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Table 2. Number of males dying according to age in cohorts born between 1983 and 2001, Somogy
county (cells yellow shaded same as in Table 1)
Table 3. Comparison of reported and calculated population size and the harvest rates of red deer males in
Somogy county (Havest rate (1) = male harvest / reported male numbers; Harvest rate (2) = male harvest /
calculated male numbers)
These kinds of changes cannot be analysed without a database like ours. It allows to study details that are
not available at a given point of time and that can only be accumulated on several decades time scales
(Rivrud et al., 2013).
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Figure 1. The reconstructed stag population of red deer and its age composition, Somogy county
Figure 2. The standardized survival program of the 1991 cohort of red deer stags, Somogy county
0123456789101112131415
0.00
0.00
0.01
0.10
1.00
age (x)
life program (lx)
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Figure 3. Changes in the survival probabilities until various ages in male red deer, Somogy county
Figure 4. Changes in the mortality of age groups of male red deer, Somogy county
1982 1984 1986 1988 1990 1992 1994 1996 1998 2000 2002
0%
10%
20%
30%
40%
50%
60%
70%
0.0%
0.5%
1.0%
1.5%
2.0%
2.5%
3.0%
3.5%
4.0%
4.5%
Survival probabilty of cohorts until the age of 6 yr, 10 yr and 13 yr (lx)
61013
l(6) and l(10)
l(13)
1982 1984 1986 1988 1990 1992 1994 1996 1998 2000 2002
0%
10%
20%
30%
40%
50%
60%
70%
Mortality of cohorts between the age of 1-5 yr, 6-10 yr
and 10-13 yr
"1-5” "6-10” "10-13”
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Figure 5. Age distribution of harvested red deer stags in Nógrád county
1990 1991 1992 1993 1994 1995 1996 1997 1998 1999 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010
0%
10%
20%
30%
40%
50%
60%
70%
80%
90%
100%
AgedistributionofreddeerstagsshotinSomogycounty
young medium old
%
Figure 6. Age distribution of harvested red deer stags in Somogy county
1990 1991 1992 1993 1994 1995 1996 1997 1998 1 999 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010
0%
10%
20%
30%
40%
50%
60%
70%
80%
90%
100%
Agedistributionofreddeerstagsshotingrádcounty
young medium old
%
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Age distribution and comparison of age groups in different counties
On the basis of the trophy scoring data it is possible to compare the contribution of different age groups to
the harvests. The proportions of young stags among evaluated trophies are considerably larger in the
'poor' habitats (counties) than in the area of 'good' habitats (Kolejanisz et al., 2012). We found that the
greatest proportions (50-80%) of young stags were shot in the counties characterized with smaller antler
sizes and less medal stags (Figure 5). In counties with the best red deer populations the proportion of
stags belonging to the young age group is smaller (35-60%; Figure 6). In better areas the medium age
group represents a higher proportion (30-50%) while in the poorer areas it is rather low (<25%).
These two counties presented here clearly differ in the quality of red deer populations. The statistical
analyses showed marked differences between the counties belonging to the poor habitats (counties) and
excellent habitats (counties). The most surprising finding was the strong difference of the age distribution
of stags shot in the 'poor' and the 'excellent' areas. In the 'poor habitats' the proportion of young stags shot
was much more compared to the 'excellent habitats' in the past decades. Proportionally fewer young stags
were shot in the excellent habitats (counties) and more in the poor habitats (counties). As far as it can be
seen , these results derive from the selective shooting of young males. In all counties young stags under a
minimum (and not defined) antler weight were removed by hunters. In the excellent areas most young
stags 'escape' early shooting since they have 'promising' antlers. As a result, in poor habitats (counties) the
proportion of young stags shot is larger than in the better habitats (counties). At the same time this is an
answer why more middle-aged stags were shot in the better quality areas (Kolejanisz et al., 2012).
Conclusion
In this presentation some of the potential uses of trophy scoring data for the analyses of population
dynamics were presented. The long-term data sets collected and stored digitally allow us to analyse
relationships and to calculate vital parameters otherwise not available. The detailed and unique trophy
scoring database allowed:
To understand the effects of variable hunting pressure on the size and composition of the red deer
populations. Reported and reconstructed population sizes can be compared and the level of
hunting pressure can be re-evaluated.
To construct cohort life tables and to relate the age-dependent survival changes to variable
hunting pressures. Although, it is not presented here, it is also possible to analyse the age-specific
changes of antler parameters in different cohorts and the relate these to variable hunting pressure.
On the basis of the trophy data sets different regions/counties can be compared. These allow the
evaluation of relationships between the red deer population quality and the hunting efforts.
Acknowledgement
The author wishes to express gratitude to the Ministry of Rural Development for the continuous support
of the National Game Management Database. This paper is supported by TÁMOP-4.2.1.B-11/2/KMR-
2011-0003 (Level up the education and research at Szent István University) and by the Research Centre
of Excellence grant (17586-4/2013/TUDPOL - Kutató Kari Kiválósági Támogatás).
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