Access the full text.
Sign up today, get DeepDyve free for 14 days.
References for this paper are not available at this time. We will be adding them shortly, thank you for your patience.
Czech Republic has a long tradition of hunting, and trophy hunting is important to manage game populations. In this study data was analysed from the five last trophy exhibitions in Czech Republic. Namely, hunter selection, compensatory selection, management selection, hunting pressure selection and depletion selection was tested in different landscape types. In compensatory hunting there is a difference between the landscape types; apparent differences exist between the landscape type with respect to hunting pressure. There was no hunter selection, or depletion selection, and no differences in management between landscape types. This study suggests that the landscape composition has an effect on selective hunting in Czech Republic. Keywords: Landscapes, Hunting selection, Czech Republic, Roe deer, Capreolus capreolus INTRODUCTION All animals occupy landscapes, and landscapes are complex mixtures of physical and cultural unit. In Czech Republic, a landlocked country in central Europe, centuries of land-use changes have altered the landscape. The changes have, for example, occurred within open landscapes like agricultural land, and artificial landscapes like towns, railways and roads. The changes have also affected the wildlife species. The opening of the landscape has also changed the act of hunting, from hunting in forests to fields, with better visual conditions for the hunter. An important species in the Czech landscape is roe deer (Capreolus capreolus). This species is distributed throughout the country, and every year, Czech hunters remove 99,000 to 121,000 specimens during the hunting season (Cervený et al., 2009). Roe deer is an important component of game hunting in Czech Republic, as well as for trophy hunting. In the Natura Viva exhibitions in Lysa nad Labem in 1996, 2000, 2005 and 2009, and the exhibition in Ceske Budejovice in 1993 inclusive, there were 2,656 roe deer trophies recorded from the entire country (CMMJ 1993, 1996, 2000, 2005, 2009). Czech Republic has a long tradition of hunting (Bartos, 2010), and the country is one of most coveted hunting countries in Europe. However, roe deer is not the most important game species in Czech Republic, but together with wild boar (Sus scrofa) the most numerous. The most important game is the red deer (Cervus elaphus) and mouflon (Ovis musimon); especially the latter, where Czech Republic has the current world record, and have most of the leading trophies in the world (Cerveny et al., 2009; Varicak, 2007). Czech Republic also has a long tradition for trophy exhibitions (local, national and international), and in the local exhibitions which are held every year, all trophies are measured. Roe deer habitat is mainly woodland, however changes in anthropogenic land use during the last millennium, with increasing urban and agricultural areas, have forced the species to utilise new habitats. Hypothesis In this study, the following hypotheses will be tested: Hunting selection: H1: Hunters selection; P1 hunters will select larger individuals; this will give a negative correlation between relative antler size and relative age (measured as standard deviation) (Rivrud, 2013; Coltman, 2003) H2: Compensatory hunting selection: P2: money talks, hunters will pay more money for larger trophies (trophy hunting) and there will be pressure to increase the quality of trophies by harvesting lower quality specimens in lower age groups (Rivrud, 2013; Mysterud & Bichoff, 2010). This is described in Czech act of game management §6.2 (Ministry of agriculture, 2001) and Babicka et al. (2007) H3: Depletion hunting selection; P3: Trophy size will decrease over years, with no recovery when the hunting pressure becomes weaker (Coltman et al., 2003; Rivrud, 2013) H4: Hunting pressure selection: P4: Trophy size will decrease over time when hunting pressure is high, however the trophy size will recover when the hunting pressure becomes weaker (Vanpe et al., 2007; Rivrud, 2013) H5: Management hunting selection: P5: Trophy size will be stable between years (Mysterud & Bischoff, 2010), however; the sign of the quadratic selection (Walsh, 2007) can calculate a weak selection (stabilisation selection or disruptive selection). A negative sign will indicate stabilisation selection and a positive sign will indicate disruptive selection. Landscapes L1: Are there differences in strength of hunting selection between landscape types in Czech Republic? PL1: L2: In respect to landscape composition (occurrence of urban, agricultural and natural/semi-natural land), how could it have an effect on hunting selection L3: The effect of edge density, patch density and mean patch size (forest) and landscape diversity on hunting selection MATERIALS AND METHODS Material The material consists of 2,640 roe deer trophies taken in Czech Republic in the period 1990 to 2008. The trophies were shown at the trophy exhibitions in Ceske Budejovice in 1993 and Lysa nad Labem in 1996, 2000, 2005 and 2009. Experts from Czech-Moravian Hunting Association (CMMJ) and the International council of wildlife conservation (CIC) measured the trophy specimens. The data from 2005 and 2009 are electronic and can be found at www.myslivost.cz, or in the trophy catalogues (CMMJ 2005, 2009). The data from 1993, 1996 and 2000 can be found in the trophy catalogues (CMMJ 1993, 1996, 2000); all trophies are medal bucks (CIC points above 105). The trophy measuring protocol can be found in Varicak (2007) or CIC red book (Whitehead et al., 1981). All stalking locations were localised by latitude and longitude of the hunting grounds in Czech Republic. (http://apps.hfbiz.cz/apps/mysliveckyportal/honitby/view/). Landscapes Corine land cover 2006 version 16 (EEA, 2012) was used, as well as Q-GIS version 2.4 (QGIS, 2014). Grids measuring 40X40 km were used, along with reference system ETRS89-LAEA (EEA, 2014). The corine classes 1.1.1 to 1.4.2 were calculated as "urban"; classes 2.1.1 to 2.4.4. were calculated to "agricultural land", classes 3.1.1 to 3.1.3 were calculated to "forest", 3.2.1 to 4.2.3 were calculated to "other natural land" and 5.1.1 to 5.1.2 were calculated to "water" (see example Estreguil et al. 2012 or appendix for details). Every grid was recalculated to a landscape type after this triangulation, see Figure 1 and Table 1 (Estreguil et al., 2012). Fig. 1: Classifications of landscape types Where these landscape types were recognised: Aun, An, Na and mix 7 Table 1: Classifications of landscape types in Czech Republic Landscape type Agricultural dominated land with some natural land (An) Agricultural dominated land with some urban and natural land (Aun) Mixed landscape (mix) Natural dominated land with some agricultural land (Na) Agricultural land 60-90 % 60-80 % Urban 0-10 % 10-30 % Natural 10-40% 0-30 % 0-60 % 10-40 % 0-60 % 0-10 % 10-60 % 60-90 % These grids was converted to geographical coordinates (WGS84) by using the internet page http://epsg/3035/map.io. The commonly-used landscape ecology indices were calculated (http://www.umass.edu/ landeco/teaching/landscape_ecology/schedule/chapter9metrics.pdf), namely edge density: where l is total length of patch type (forest and transitional wooded scrub) and A is total landscape area (160000 ha) per grid. Patch density n/A was used, where n is number of patches (forest, wooded transitional scrub) and A is total area. Mean patch size Ai/n, where Ai is area of forest and transitional scrub and n is number of patches. Finally, Shannon diversity index was used to calculate landscape diversity. Where m = number of patch types Pi = proportion of area covered by patch type (land cover class) All landscape calculations were completed in the LecoS module in QGIS (QGIS, 2014). Hunter selection was calculated in the NUTS regions in Czech Republic and correlated to edge density, patch density, mean patch size and landscape diversity. Hunting selection The hunter selection was calculated (standardised CIC) with antler size and age as the independent and dependent variable, respectively. A negative slope indicates a negative hunter selection, whereas a positive slope indicates a positive hunter selection. For landscape type and for NUTS regions: CZ02: Central Bohemia; CZ03: Southwest Bohemia (Plzen and South Bohemia regions); CZ04: Northwest Bohemia (Carlsbad and Usti regions); CZ05: Northeast Bohemia (Pardubice, Hradec Kralove and Liberec regions); CZ06: South Moravia (South Moravia and Vysocina regions); CZ07 : Central Moravia (Olomouc and Zlin regions); CZ08: Silesia-Moravia, CZ01: Prague is excluded from the analysis. Compensatory hunting selection Selective hunting figures in Czech Republic are shown in Table 2 (after Babicka et al., 2007). Table 2: Selection for breeding of roe deer in Czech Republic in different age classes Age Beam length Number of tines 1 10 cm 2 1 7 cm 3 2 15 cm 4 2 15 cm 5 3 20 cm 6 4-5 22 cm 6 6 23 cm 6 Age above 6 years old, no restrictions Tine length 1 cm 3 cm 2 cm 3 cm 4 cm 5 cm Auxiliary characters breeding The benefit is for 2-year-old is considered a length of 1.5 cm, with 3-year older , 2 cm in length, extremely high quality in sense of high pearling, strong coronets, regularity of antlers, exceptionally high mass and force antler is a significant feature of breeding at lower length of beams, with annual roe deer does not consider pucks under 1 cm for the tines For analysing compensatory hunting, the percentage of antler size (1.28 Standard deviation of the CIC) was used, and the percentage in each landscape type was analysed. An ANOVA was calculated for landscape type as factor and age as covariate, and number of +1.28 standard deviation (close to the gold challenge in the CIC system) trophies (trophy hunting) and 1.28 standard deviation (compensatory hunting). A t-test was used to compare the different landscape types. See appendix for details. Management hunting selection Linear regression CIC (standardized): , was used to analyse how stable the quality has been over the years, and quadratic regression distinguished stable and disruptive selection: CIC (standardized) = , where are constants. If the quadratic component is negative, it is stabilisation selection, and if positive, it is disruptive selection (Walsh, 2007). RESULTS Landscapes The results are shown in Figure 2. Out of 66 grids, 30 grids where classified as mixed landscape (mix); 29 grids were agricultural dominated land with natural land (An); four grids where classified as agricultural dominated land with urban and natural land, and three grids where classified as natural dominated land with agriculture (Na). The landscape grids are huge (1,600 square kilometers), so it could be variation within every grid, but it gives a rough picture of the Czech landscape composition. Fig. 2: Landscapes in Czech Republic Table 3: Regressions of hunter selection Regression An Aun Mix Na CZ02 CZ03 CZ04 CZ05 CZ06 CZ07 CZ08 intercept -0.041 -0.008 0.047 0.117 0.300 0.080 -0.038 0.143 -0.074 -0.241 -0,042 slope 0.017 -0.047 -0.012 0.080 0.016 0.064 0.004 0.012 -0.005 -0.073 -0.148 Standard error 0.027 0.047 0.036 0.178 0.056 0.047 0.079 0.045 0.047 0.041 0.073 t-test 0.980 -0.963 -0.348 0.350 0.290 1.369 0.046 0.259 -0.099 -1.761 -1.994 p-value 0.552 0.336 0.728 0.727 0.770 0.172 0.963 0.796 0.922 0.079 0.048 Significant (p-value <0.05) No No No No No No, but weak trend No No No Trend yes Hunter selection Results are shown in Table 3. The results show no hunters selection in landscape types, or in NUTS regions; however it is a trend in Central Moravia (CZ07) and significant negative selection in Moravia-Silesia. Other regions do not show a trend for hunter selection. Complementary hunting The results are shown in Tables 4a, 4b and 5 and in Figure 3. Fig. 3: Trophies in age and quality in the landscape types Table 4a: ANOVA compensatory hunting Low quality trophies (compensatory hunting) Source of Variation Landscape Age Residual Total DF 3 5 15 23 SS 14,792 13,708 24,458 52,958 MS 4,931 2,742 1,631 2,303 F 3,024 1,681 P 0.062 0.200 The difference in the mean values among the different levels of landscape is not great enough to exclude the possibility that the difference is due only to random sampling variability, after allowing for the effects of differences in age. There is not a statistically significant difference (P = 0.062), however is it a trend. The difference in the mean values among the different levels of age is not great enough to exclude the possibility that the difference is due only to random sampling variability, after allowing for the effects of differences in landscape. There is not a statistically significant difference (P = 0.200). Table 4b: High quality trophies (trophy hunting) Source of Variation Landscape Age Residual Total DF 3 5 15 23 SS 1791,500 949,333 851,000 3591,833 MS 597,167 189,867 56,733 156,167 F 10,526 3,347 P <0.001 0.031 The difference in the mean values among the different levels of landscape is greater than would be expected by chance, after allowing for effects of differences in age. There is a statistically significant difference (P = <0.001). A multiple comparison procedure was used to isolate which group(s) differ from the others. The difference in the mean values among the different levels of age is greater than would be expected by chance, after allowing for effects of differences in landscape. There is a statistically significant difference (P = 0.031). Table 5: Comparing landscape types and compensatory hunting Comparing landscape types An vs Aun An vs mix An vs Na Aun vs mix Aun vs Na Mix vs Na t-test 1.484 0.783 1.645 1.949 2.101 1.418 Degrees of freedom 1654 2118 1377 1138 397 861 p-value 0.138 0.434 0.100 0.052 0.036 0.157 Trend Not significant Trend Strong trend Significant Trend Management hunting selection The results are shown in Table 6 Table 6: Management hunting selection Landscape Intercept slope type -15.146 0.008 An (linear) -6589.40 6.584 An (quadratic) -42.79 0.021 Aun (linear) -18646.2 18.63 Aun (quadratic) 13.31 -0.007 Mix (linear) -3452.22 3.46 Mix (quadratic) 14.909 -0.008 Na (linear) -14942.6 14.95 Na (quadratic) (x - but trend for increasing) Quadratic SE 0.004 -0.002 0.011 -0.005 0.006 -0.001 0.017 -0.004 -0.449 0.681 1.087 0.834 1.922 3.926 t-test 1.689 2.951 F-ratio p-value 0.091 0.053 0.055 0.021 0.277 0.435 0.449 0.510 Yes Stabilisation Yes Stabilisation Yes, (x) Stabilisation Stable Yes, (x) Stabilisation Disruptive or Stabilisation The trophy qualities are stable from year-to-year in all landscape types, however there is a trend for increasing quality in agricultural dominated landscapes (Easier to manage?) The selection is stabilisation in all landscape types. Depletion hunting selection Number of entries per year (harvested trophies) is showed in Figures 4-6 and Table 7. Table 7: Identification of the breaking point by using piecewise regression Results for the Overall Best-Fit Solution: R 0.8179 Rsqr 0.6690 Adj Rsqr 0.6028 Standard Error of Estimate 44,1090 t 4,5063 2,0937 10,4241 1459,7584 P 0.0004 0.0537 <0.0001 <0.0001 y1 y2 y3 T1 Coefficient 143,8571 46,3627 241,0000 1995,6872 Std. Error 31,9238 22,1444 23,1194 1,3671 Analysis of Variance: DF SS Regression 4 389519,8956 Residual 15 29184,1044 Total 19 418704,0000 MS 97379,9739 1945,6070 22037,0526 Corrected for the mean of the observations: DF Regression 3 Residual 15 Total 18 SS 58991,6851 29184,1044 88175,7895 MS 19663,8950 1945,6070 4898,6550 F 10,1068 P 0.0007 Decreasing in the period 1990-1995, and increasing in the period 1996-2008. Fig. 4: Number of entries between 1990-2008, the slope marking the breaking point in 1996 Number of entries year ANCOVA period 1990-1995 Adjusted M eans with 95% Confidence Intervals Fig. 5: Adjusted Means with 95% Confidence Intervals 0,4 0,2 0,0 -0,2 standard CIC -0,4 -0,6 -0,8 -1,0 -1,2 mix An Aun Na Landscape type There are no significant differences between landscape types during the period 1990-1995. ANCOVA: period between 1996-2008 Adjusted M eans with 95% Confidence Intervals Fig. 6: Adjusted Means with 95% Confidence Intervals 0,4 0,2 0,0 standard CIC -0,2 -0,4 -0,6 -0,8 Aun An mix Na Landscape type Natural land shows negative slope during the decreasing and increasing periods; this is some indication for depletion in this landscape type. Hunting pressure hunting selection 1990-1995 (decreasing hunting pressure) Young bucks (below 4 years old) Period 1990-19 95 Age group Young (age<4 years) Landscape An Aun Mix Na An Aun Mix Na An Aun Mix Na intercept Significant (p<0.05) 309.85 -0.156 0.090 -1.728 0.090 trend 1.843 -0.001 0.146 -0.007 0.995 No -577.66 0.290 0.202 1.434 0.177 trend Due to few observations, the analysis was not able to be carried out during this period 20.15 -0.010 0.050 -0.203 0.839 No -163.99 0.082 0.090 0.908 0.370 No 257.88 -0,129 0.052 -2.032 0.046 yes -296.48 0.149 0.097 1.531 0.223 No -82.34 0.041 0.052 0.816 0.416 no -35.10 0.017 0.136 0.129 0.899 no 38.99 -0.020 0.077 -0,253 0.801 No -11,78 0.006 0.130 0.043 0.973 No slope SE t-test p-value Medium (4 and 5 years old) Old (age >6 years 1996-2008 (increasing hunting pressure) Period 1996-20 08 Age group Young (age<4 years) Medium (4 and 5 years old) Old (age >6 years Landscape An Aun Mix Na An Aun Mix Na An Aun Mix Na intercept 45.898 189.68 45.737 2.155 32.96 -5.60 39.48 24.45 35.13 67.00 40.71 73.265 slope -0.023 -0.095 -0.023 -0.001 -0.016 0.029 -0.029 -0.012 -0.018 -0.033 -0.020 -0.037 SE 0.021 0.047 0.021 0.078 0.013 0.009 0.016 0.028 0.017 0.037 0.019 0.044 t-test --1.091 -2.017 -1.112 -0.016 -1.305 0.100 -1.232 -0.447 -1.015 -0.908 -1.056 -0.836 p-value 0.277 0.051 0.269 0.988 0.193 0.921 0.219 0.660 0.311 0.367 0.292 0.416 Significant (p<0.05) no Strong trend No No trend No No No No No No No 8 out of 11 shows a more negative slope in the increasing period, so the hypothesis is partially supported. Correlation matrix hunting selection coefficients (HUNS) vs landscape factors and landscape indexes % Urban HUNS -0.243 % Agriculture land -0.076 % Forest 0.111 Other natural land 0.191 % Water -0.010 Edge density 0.013 Patch density -0.096 Mean patch size 0.268 Shannon diversity index 0.051 The results show negative hunter selection in areas with high occurrence of urban areas, agricultural areas and high patch density. Positive hunting selection was identified in areas with high occurrence of forest, other natural land and how diverse the landscape is. DISCUSSION In recent years, there have been several studies concerning the topic of hunting selection (examples include Mysterud, 2011, 2007; Rivrud et al., 2013; Hedrick, 2012; Festa Bianchet, 2003). One of the most complete studies, Rivrud et al. (2013), concerning a long term study of red deer in Hungary, identified a support for hunter selection since trophy hunters shoot larger males. In this study, it was found that foreign hunters took 76.36 percent of harvested 9 year old bucks, although there was considerable variation. A study by Festa-Bianchet, however, claims that horn size in big horn rams (Ovis canadiensis), was decreased by hunter selection. In this study, significant hunter selection was not identified in the landscape types; within the Moravia-Silesia region there was a negative hunter selection, in central Moravia NUTS region there was a trend for negative hunter selection, and in Southwest Bohemia there was a trend for increasing quality by hunter selection. Mysterud (2011) examined 26 studies in the literature and only three of these studies lead to directional selection. Monteith et al. (2013) analysed different game species under the Boone and Crocket system used in North America, and found positive temporal trends in Canada moose (Alces a. americana and A.a.andersoni), Muskox (Ovibovis moschatus), Pronghorn (Antilocapra americana), and Rocky mountain goat (Oreamnos americanus). Other horned and antlered game showed 16 negative trends, but found no support for desire to submit smaller, yet eligible trophies, but found a support for selective harvest against genes for large trophy specimens. The results show a negative correlation between hunter selection coefficients and the occurrence of urban areas, agricultural areas and patch density. Within agricultural areas, the landscape is open and the hunter would have time to take a selective decision before shooting an animal. Higher patch density will not give the roe deer enough cover, and if the hunter is waiting close to the paths between the patches, he could have enough time to take a selective shot. Urban areas, however, or more precisely closeness to urban areas, or other artificial constructions, enable the hunter to access the hunting grounds more easy and spend more time in the field. In the forests, the time for selection is shorter; however, there is also a target for the gamekeeper to increase the quality of game within forest habitat. Patch size will provide more cover and reduce the time for selection. There are surprisingly few studies concerning the landscape effect of selective hunting. Mysterud et al. (2006) found a difference between local and foreign roe deer hunters in Poland; local hunters were hunting within closer proximity to the forest than foreign hunters, which preferred agricultural areas. However, more research is necessary, especially in the species mouflon and red deer that are the most attractive game species in Czech Republic. The Czech act of game management supports compensatory hunting, and the Czech Moravian hunting association (CMMJ) described the criteria for breeding (Babicka et al., 2007). This study supports this, however, there is a difference between the landscape types, where agricultural land differs from mixed landscape and natural landscape. A possible explanation for this could be that it is easier to manage roe deer in the agricultural landscape, or that the agricultural landscape is more attractive to roe deer hunters than other landscapes. A combination of both theories is likely, because there are more roe deer in the agricultural landscape and therefore a higher probability for a successful hunt. Due to the openness of the landscape, the gamekeepers can more easily select bucks for breeding. That does not mean that the hunter cannot select a large trophy buck, but it is expensive, a gold medal buck in Czech Republic costing in excess of 2000 euro (http://elovni.cz). Rivrud et al. (2013) found that, in Hungary, foreign hunters harvest larger stags than local hunters. Hungary has much of the same hunting rules and hunting traditions as Czech Republic. Mysterud & Bischoff (2010) developed a model for compensatory hunting without negative effects of trophy hunting, where low quality individuals can be harvested during an early life stage to facilitate sustainable trophy hunting. Babicka et al. (2007) shows this in practice. Trophy quality was consistent from year-to-year, however in in the agricultural areas there is a trend for increasing trophy quality. It is also suggested that the stability is a result of stabilisation selection, not disruptive selection. Rivrud et al. (2013) supported this, particularly in Hungary, but with periods of decline, it was not always the case. In the period 1990 to 1995 there was a declining in number of entries, which could be an effect of decreasing population. In 1996 to 2008 the population increased. This followed a decrease in hunting pressure between 1990-1995, which subsequently increased during 1996 to 2008. Differences between or within landscape types were not found with respect to depletion, so the depletion hypothesis is therefore rejected. The same was found during a red deer study in Hungary (Rivrud et al., 2013). During 1990 to 1995, in young bucks there was a negative trend in the agricultural dominated with natural landscape, but in mixed landscape there was a positive trend in younger bucks. In medium aged bucks in a mixed landscape, there was a significant decline. In the period 1996 to 2008 there was negative trend in young bucks in agricultural dominated land with natural and urban, and medium sized bucks in agriculture dominated land with natural land. However, 8 of 11 age/landscape classes did have a stronger negative trend in the period 1996 to 2008, so the hunting pressure hypothesis cannot be rejected, but neither supported. Other factors Environmental selection can also affect antler quality. Climatic variation, especially severe winters (cold and snowy winters) will decrease the surplus of antler development, and mild winters will increase the antler quality. Heldrick (2012) analysed desert big horn, and the decline was not only caused by hunting selection, but also variation of rainfall and inbreeding depression. Other factors could be population dynamics, where high density produced more density-dependent competition and lower trophy quality. This is because changes in the landscape (namely changes in land use) will affect the quality of habitats; like home range, nutrient quality and cover, however more research is required to investigate this topic. Josef Hromas (1998) investigated what could have effects on trophies in the former Czechoslovakia; soil type, forest type, climate (temperature, precipitation, days of snow cover, snow depth, clear days and length of growth season) will all affect the development of trophy specimens. An interesting question is; can researchers use trophy books as a source to investigate trends in populations. Pellitier et al. (2012) claims that data from selective harvest underestimate temporal trends in quantitative traits. This question was discussed at the 60th CIC general assembly in Budapest in 2013, and Professor Csanyi (pers. comm. 2013) claimed that the trophy books can be used; they are biased, but contain valuable data, which can be used over successive years. Implications for management and conclusion The effect of trophy hunting has grown to be an important field of wildlife biology the last decade. The works of Professor Mysterud and Professor Festa-Bianchet have been important in increasing understanding of this topic. However, all the processes work together in management of game species, and it is desirable to make a model that takes population dynamics, habitat quality, landscape ecology, climate data and hunting selection into consideration. It could be expressed by making a structural equation model (SEM). Using this model, it is possible to make decisions at a local or regional level with respect to trophy quality. The landscape will have an effect on selective hunting, and open areas like agricultural areas will make it easier to make decisions. However, more research is necessary to understand the dynamics between game management and landscape ecology. ACKNOWLEDGEMENTS I wish to thank Ing. Ludek Kralicek, Czech- Moravian Hunting Association, for data.
Journal of Landscape Ecology – de Gruyter
Published: Jan 29, 2015
Access the full text.
Sign up today, get DeepDyve free for 14 days.