A Demographic Perspective on the Spatial Behaviour of Hikers in Mountain Areas: the Example of Berchtesgaden National Park
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Research eco.mont – Volume 9, special issue, January 2017 66 ISSN 2073-106X print version – ISSN 2073-1558 online version: http://epub.oeaw.ac.at/eco.mont https://dx.doi.org/10.1553/eco.mont-9-sis66 A demographic perspective on the spatial behaviour of hikers in mountain areas: the example of Berchtesgaden National Park Johannes Schamel Keywords: demographic change, spatial behaviour, GPS tracking, outdoor recreation, Berchtesgaden NP Abstract Profile In Germany, as in many Western societies, demographic change will lead to a higher Protected area number of senior visitors to natural recreational areas and national parks. Given the high physiological requirements of many outdoor recreation activities, especially Berchtesgaden NP in mountain areas, it seems likely that demographic change will affect the spatial behaviour of national park visitors, which may pose a challenge to the manage- ment of these areas. With the help of GPS tracking and a standardized questionnaire Mountain range (n=481), this study empirically investigates the spatial behaviour of demographic age brackets in Berchtesgaden National Park (NP) and the potential effects of demo- Alps graphic change on the use of the area. Cluster analysis revealed four activity types in the study area. More than half of the groups with visitors aged 60 and older belong Country to the activity type of Walker. Germany Introduction transferable to mountain areas. Rupf (2015, 170) and Trachsel and Backhaus (2011) found that older visitors In Germany and other European countries, demo- prefer shorter and less demanding trips when hiking in graphic change already affects different aspects of so- the Alps, but results were drawn from data on stated ciety. The Federal Statistical Office forecasts that Ger- preferences, not actual spatial behaviour. many’s population will age rapidly in the next years When uncovering differences in spatial behaviour and the share of people aged 60+ will rise from 27.4% between age brackets, one has to keep in mind that in 2014 to 36.7% in 2040 (Destatis 2015). age has not an influence per se, but it can serve as a Managers of protected areas, especially in moun- proxy variable on all age-related impacts, like increas- tain areas, have to rethink all aspects of visitor man- ing health restrictions that shape the spatial behaviour agement, including educational programmes, activi- of the visitors (Breuer et al. 2010). ties offered and lastly their infrastructure to meet the In the context of demographic change, several needs of future seniors (Eagles 2007). Therefore it studies forecast future behaviour of age brackets is crucial to understand the connection between age based on a cross-sectional study (Bowker et al. 2006). and the spatial and temporal behaviour of visitors, as This approach has weaknesses, as age, cohort and pe- an effective visitor management should be based on riod effects occur simultaneously in cross-sectional sound distribution data (Hallo et al. 2012; Job 1991). studies (Pennington-Gray et al. 2002). Nevertheless, in The spatial behaviour of outdoor recreationists the absence of appropriate longitudinal data, forecast- like hikers is influenced by many factors. According ing future behaviour based on a cross-sectional study to Beeco and Hallo (2014) these can be grouped into can serve as the second best alternative if theoretical the following categories: visitor personal characteris- reasoning is possible why a dominant age, period or tics, user group type, knowledge of destination, re- cohort effect can be assumed (Bowker et al. 2012). sources and constraints and the infrastructure of the Therefore several research questions emerged for area. Various studies confirmed the relevance of these the study area Berchtesgaden NP: How can visitors factors, for example, the influence of visitor personal pursuing activities on foot be segmented according characteristics like motivation and skill (Beeco & Hal- to their spatial behaviour? Are there turning points in lo 2014; McFarlane et al. 1998; Farias Torbidoni et al. the life cycle, where spatial behaviour changes? How 2005; Meijles et al. 2014; Wolf & Wohlfart 2014), the might frequentation of trails be affected by demo- role of previous knowledge (Beeco et al. 2012; McFar- graphic change? lane et al. 1998) or the influence of the infrastructure (Taczanowska 2009; van Marwijk 2009). Arrowsmith Methods and data et al. (2005) found that senior visitors stay shorter and cover less distance – a pattern that was also followed Study area by groups with small children (Meijles et al. 2014). Berchtesgaden NP is the only Alpine NP in Ger- In contrast, Beeco and Hallo (2014) did not find any many and covers an area of 208 km². The elevation correlation between age and trip length. However, all ranges from Lake Königssee (603 m) to Mount Watz- studies were conducted in flat areas and results are not mann (2 713m) and hiking infrastructure consists of Johannes Schamel 67 Table 1 – Visitor characteristics by age group. a refers to group; b refers to group leader / respondent; c rated on a scale 1 (best) to 5 (worst), refers to maxi- mum in group; d refers to youngest group member if it is younger than 15, otherwise oldest group member; * p < 0.05. ** p < 0.01. *** p < 0.001; e Chi-square test; f ANOVA; g Kruskal-Wallis-test Under 15d 15 to 39d 40 to 49d 50 to 59d 60 to 69d 70 and olderd Total Test n 67 85 73 100 90 66 481 All-malea 7.5% 31.0% 34.2% 18.0% 5.6% 30.8% 20.7% Chi2 = 52.7***e All-femalea 6.0% 9.5% 8.2% 7.0% 0.0% 1.5% 5.4% Mixeda 86.6% 59.5% 57.5% 75.0% 94.4% 67.7% 73.9% Day tripperb 13.4% 36.9% 18.1% 13.9% 13.5% 16.7% 18.8% Chi2 = v22.8***e Overnight stays in the region (only overnight visitors)b 5.2 4.0 4.7 7.4 7.2 7.6 6.2 F = 6.3***f – recreateb −0.12 0.25 0.26 0.04 −0.15 −0.44 0 F = 3.4** f – discover sth. newb −0.38 −0.30 −0.21 0.05 0.16 0.91 0 F = 11.2*** f – experience natureb −0.16 0.12 −0.03 0.13 −0.44 0.43 0 F = 4.91* f – sport and fitnessb −0.81 0.18 0.15 0.09 0.18 0.16 0 F = 8.6*** f – be with family and friendsb 0.58 −0.10 −0.36 0.12 0.07 −0.38 0 F = 6.7*** f b *** f Motivation factor score – do sth. exciting 0.26 0.83 0.06 0.03 −0.73 −0.67 0 F = 22.7 endurance-indexc 3.7 2.7 2.8 3.2 3.5 4.0 3.3 F = 27.7*** f surefootedness-indexc 2.7 1.9 2.0 2.1 2.5 3.2 2.3 F = 19.5*** f absence of vertigo-indexc 3.2 2.5 2.6 2.9 2.9 3.1 2.9 F = 3.8**g, f c 2 ***g Self-rated health restrictions 1.7 1.4 1.9 2.0 2.5 3.5 2.1 Chi = 95.5 more than 250 km of marked hiking trails, alpine huts, seconds. Visitor characteristics were obtained with a as well as three means of transportation (see Figure 3) standardized questionnaire and matched to spatial be- (Vogel 2011). haviour with a common key variable. If groups were 1.58 million visitors were counted in the area in encountered, the interview was conducted with the 2014, which means a strong rise in visitor numbers person who was mainly responsible for the planning since 2002 (Metzler et al. 2016). Concentrations of of the trip. In total, 676 GPS trajectories with corre- visitors occur around Lake Königssee as well as at sponding questionnaires were collected. After deduct- Mount Jenner and almost 95% of the visitors discover ing tranjectories with loss of GPS signal, logging of the NP on foot. other activities like backcountry skiing, or cases of re- Multiple reasons were decisive for selecting this turned questionnaires of poor quality, the sample size study area. It provides a broad range of acitvities on was reduced to n2 = 481 (71.1%). foot at different skill levels. As a NP it is likely to see only very limited or no changes in the hiking infra- Trail network and points of interest structure, which is useful for simulating scenarios. A geo-database with additional information about Lastly, the area is in a dead end situation with only a the trail network was constructed in ArcGIS 10.2.2. limited number of access points. Slope of the trails was calculated based on a 10 m Dig- ital Elevation Model of the area. Trail difficulty was Data collection rated by two local experts in one of four categories: Several types of information are needed to answer barrier-free trails (scale 1), trails with an even surface the research questions. Manual counts, including ran- without danger of falls (scale 2), trails with rough sur- domly sampled short interviews (n1 = 9 460) cover- face without danger of falls (scale 3), trails with danger ing duration of stay and age were conducted on 20 of falls (scale 4). Land use along trails was classified days throughout the year at seven main access points. in five distinct categories: forest, grassland, rock, lake- Based on these counts, we calculated the total number side paths and infrastructure. Trails were classified as of visitor days per age bracket, interview location and lakeside paths if they were within 50 m of bigger wa- season following the methodology of Job et al.