The reconstructed stag population of red deer and its age composition, Somogy county 

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... 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. 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, 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. 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 Tóth, 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). 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 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 ). 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., ...