Longterm Impacts of Ski Piste Management on Alpine Vegetation

Journal of Applied Ecology 2011, 48, 906–915 doi: 10.1111/j.1365-2664.2011.01964.x Long-term impacts of ski piste management on alpine vegetation and soils

Philippe Roux-Fouillet1,2, Sonja Wipf1,3 and Christian Rixen1*

1WSL Institute for Snow and Avalanche Research SLF, Alpine Ecosystems, Flu¨elastr. 11, 7260 Davos, Switzerland; 2Soil & Vegetation Laboratory, University of Neuchaˆtel, Rue Emile-Argand 11, C.P. 158, 2000 Neuchaˆtel, Switzerland; and 3Federal Institute for Forest, Snow and Landscape Research WSL, Unit Soil Sciences, Team Biogeochemistry, Zu¨rcherstr. 111, 8903 Birmensdorf, Switzerland

Summary 1. Downhill skiing, the machine-grading of slopes and the use of artificial snow induce major disturbances to the environment of alpine ski resorts. Our study aims to quantify the impacts of different ski piste management types (graded⁄ungraded; with⁄without artificial snow) on the environment and its development over time. 2. We re-sampled study plots established 8 years earlier and compared vegetation and soil characteristics on different types of ski pistes to adjacent off-piste control plots, and analysed vegetation changes over time. 3. Generally, machine-grading led to a decreased plant cover and plant productivity, and increased indicator values for nutrients, light and soil base content compared to control plots. Ungraded ski pistes and artificial snow led to increased vegetation indicator values for nutrients and soil humidity. 4. Soil analyses conducted in 2008 generally confirmed the changes shown by the vegetation indicator values in 2000 and in 2008. Machine-grading had the greatest effects on soil characteristics by increasing soil density by more than 50%, by increasing pH and C ⁄N ratio, and by decreasing total nitrogen concentrations. 5. The differences between piste and off-piste plots were similar to those found 8 years ago, but their proportions changed. The vegetation cover on machine-graded ski pistes decreased over the 8 years, showing no sign of recovery or succession. Ungraded ski pistes showed increased differences in indicator values for reactivity and humus between piste and control plots compared to the results obtained 8 years earlier. 6. Synthesis and applications. Machine-grading of ski runs and downhill skiing in general induced long-lasting impacts on vegetation and on both chemical and physical soil characteristics. Even though few impacts of artificial snow were significant, our results suggest that it may change moisture status of the vegetation, and thus caution is warranted when used in dry and nutrient-poor habitats. The vegetation cover on machine-graded pistes deteriorated over a period of 8 years, illustrating that natural recovery did not occur in these alpine habitats. Consequently, the construction of new pistes by machine-grading in alpine habitats should be avoided, and existing pistes should be managed to avert further disturbances. Key-words: alpine vegetation, artificial snow, biodiversity, long-term impact, machine-grading, soil, vegetation recovery, winter tourism

Isselin-Nondedeu & Be´de´carrats 2007). In the Swiss Alps Introduction alone, 220 km2 are directly aﬀected by ski pistes (Amacher- Ski resorts attract millions of visitors and represent a major Hoppler & Schoch 2008). The construction and use of ski economic factor in alpine regions (Elsasser & Messerli 2001; pistes severely alter landscape aesthetics and potentially threa- ten the fragile high-mountain ecosystem biodiversity and ero- *Correspondence author. E-mail: [email protected] sion control (Tsuyuzaki 1995; Rixen 2002; Wipf et al. 2005;

2011 The Authors. Journal of Applied Ecology 2011 British Ecological Society Ski piste impacts on vegetation and soil 907

Delgado et al. 2007; Burt & Rice 2009; Pohl et al. 2009). These nutrient- and moisture-demanding species (Kammer & Hegg ecosystems are typically highly sensitive towards changes in 1990; Wipf et al. 2005). Moreover, the increase in meltwater environmental conditions and have low resilience after distur- can enhance soil erosion rates (Isselin-Nondedeu & Be´dećar- bance (Ko¨rner 1999). Ski pistes are often visible in the land- rats 2007). As artificial snow is applied every year, a cumulative scape during summer due to damaged vegetation, which also effect over time is likely (Kammer 2002) but long-term com- impedes tourism. parative studies are needed. Building and maintaining ski resorts often incorporates the The present study aimed to evaluate the impacts of use of heavy machinery during summer for the levelling and ungraded ski pistes, graded pistes and pistes with artificial drainage of the slopes. Many ski pistes are machine-graded to snow on vegetation composition over an 8-year time period smooth the slope surface, whereby rocks and obstacles, but and on important soil parameters. The study was based on also the natural vegetation and most of the organic topsoil, are a pair-wise design of permanent plots situated on and next removed, resulting in major disturbance to plant and soil com- to ski pistes located in 12 Swiss ski resorts. In each plot, the position (Mosimann 1985; Wipf et al. 2005; Isselin-Nondedeu vegetation composition was recorded along with soil physi- &Be´dećarrats 2007; Delarze & Gonseth 2008). What remains cal and chemical aspects. Our sampling was a re-visitation after grading is usually a mineral substrate with low organic of plots from a previous vegetation study (Wipf et al. 2005); matter content and poor water holding capacity (Krautzer therefore longer-term impacts (8 years) of ski piste manage- et al. 2006; Burt & Rice 2009). Plant cover and diversity play a ment were evaluated by comparing results from the two major role in retaining sediments and nutrients in alpine eco- studies. systems (Isselin-Nondedeu, Rey & Be´dećarrats 2006; Pohl We expected that ski pistes in general, and graded ski pistes et al. 2009) but natural re-vegetation processes on these min- in particular, would decrease vegetation cover, vegetation pro- eral soils are difficult and slow (Barni, Freppaz & Siniscalco ductivity and species richness as found previously (Wipf et al. 2007). 2005). We also expected changes in vegetation characteristics During winter, snow-grooming vehicles and skiers com- due to ski piste management to be reflected in soil chemical pact the snow cover and potentially damage vegetation and and physical properties, especially soil bulk density. Over the soil. The compaction of snow decreases the thermal insula- course of 8 years, we expected that impacts of artificial snow tion and gas exchange capacity of the snow cover (Rixen, would increase. As machine-grading is a one time treatment, Stoeckli & Ammann 2003; Delgado et al. 2007), which can graded pistes were expected to show signs of succession and lead to frost damage of plant vegetative parts and fine roots vegetation recovery over time. (Tierney et al. 2001; Rixen, Stoeckli & Ammann 2003). However, little is known about how vegetation may respond Materials and methods to these perturbations over the long term. Only a few studies have looked at changes in soil processes under ski pistes STUDY SITES (Freppaz et al. 2002; Burt & Rice 2009), and even less is known about the combination of soil and vegetation In 2000, permanent plots were chosen in pairs consisting of a 4 · 4m piste plot and an off-piste control plot of the same area, and were responses under these conditions. Furthermore, many con- revisited for the purpose of the present study. The pairs were located clusions about impacts of skiing have been drawn from case on 37 ski runs differing in altitude (ranging from 1700 to 2500 m studies in single ski resorts (exceptions: Wipf et al. 2005; a.s.l.), aspect (8–30) and bedrock (acidic and calcareous) and were Burt & Rice 2009), whereas studies from multiple locations situated in 12 resorts in the Swiss Alps (for details on plot location are needed for conclusions of general value. and ski piste characteristics see Wipf et al. 2005). The vegetation on Sufficient snow cover from early winter to spring is very most sites consisted of alpine grassland and dwarf shrub heath. The important for the economic success of alpine ski resorts (Elsas- plots established in the previous study (Wipf et al. 2005) had been ser & Messerli 2001). To mitigate climate change effects and to marked with 25-cm-long nails at two edges and referenced to paint guarantee sufficient snow, a rising number of ski pistes are marks on nearby rocks; thus they could be relocated using GPS coor- equipped with snow-making facilities (33% of ski pistes in dinates, photographs, tape measures and compass. Switzerland, 59% in Austria, and 70% in Italy in 2008; Am- For each visited ski piste, one plot was randomly chosen on the piste and one control plot next to the piste. The ski pistes were either acher-Hoppler & Schoch 2008). Artificial snow possesses dif- machine-graded (i.e. removal of rocks and topsoil, performed ferent physical and chemical properties than natural snow 5–38 years ago), or ungraded, and had either natural or artificial (Fauve, Rhyner & Schneebeli 2002), leading to both denser snow (snow production applied for 2–23 years) resulting in four dif- and deeper snow cover on ski runs (Rixen 2002). Due to the ferent types of treatments: natural snow ⁄ ungraded (6 pairs of plots in additional amount of snow, more water is released during 2008, 11 in 2000); artificial snow ⁄ ungraded (13 pairs of plots, 10 in snow melt in spring (Mosimann 1998), and the postponed 2000); natural snow ⁄ graded (7 pairs of plots, 9 in 2000); and artificial snow melt shortens the plant growing season by up to 4 weeks snow ⁄ graded (11 pairs of plots, 8 in 2000). The control plots were at (Rixen 2002). As the water for snow production is commonly most 50 m away from their piste plots and had the same aspect, slope taken from streams or lakes, it contains more ions and nutri- and altitude, but they were not affected by the construction or the ents, and thus has a different pH than rain water (Kammer & snow treatment. Analyses of the 2008 data were carried out on the Hegg 1990; Rixen 2002). Changes in vegetation composition entire 37 pairs of plots. The comparison between 2000 and 2008, however, could only be done with a reduced data set of 31 pairs of plots have been observed where artificial snow is used, favouring

2011 The Authors. Journal of Applied Ecology 2011 British Ecological Society, Journal of Applied Ecology, 48, 906–915 908 P. Roux-Fouillet, S. Wipf & C. Rixen

due to treatment changes on six ski pistes. Soil samples were taken at c. 4 months and then analysed for total nitrogen (Ntot) and total the sites of the vegetation releve´s. phosphorus (Ptot) according to the KJELDAHL method (Baize 2000)

and soil acidity (pHKCl, as opposed to reactivity derived from the plant indicator values). Due to the delay of these soil measurements, VEGETATION DATA individual values could not be compared to the literature. Only the Releve´s of vascular plants were made in 74 plots between 1 July differences between piste and off-piste values were analysed according and 20 August 2008 following the protocol of Wipf et al. (2005). to the pair-wise design of our study. Total organic carbon (TOC) was Total vegetation cover was estimated in each plot, and the cover measured using a Rock-EvalTM pyrolysis analyser (IFP, Paris, of each plant species was recorded in seven classes according to France) taking in account eventual soil CaCO3 contents. One pair of Braun-Blanquet (1932). For each plot, species richness, plant spe- plots had to be omitted from chemical analysis due to a lack of the cies diversity (Shannon Index) and weighted indicator values for fine soil fraction. humidity, light, nutrient availability, reactivity (soil base contents) Additionally, two soil cores were taken in each plot using a 30-cm- and humus (Landolt 1977) were calculated. These values define long cylindrical corer (d = 5 cm) to determine soil bulk density by the preferred conditions for the growth of each plant species. dividing dry mass by volume. In 10 pairs of plot, that contained only The weighted values thereby give indications of ecological condi- scree and rocks, the soil density cores could not be sampled. Soil char- tion in each plot. Along with these values, the cover of four dif- acteristics could only be compared between piste and control plots ferent functional groups (graminoids, forbs, legumes and woody and not over time as no soil analyses were made in 2000. species) was calculated. We classified each plant species depend- ing on their flowering period (‘early’ being before July, ‘late’ after STATISTICAL ANALYSIS July and ‘other’ for other flowering periods; Aeschimann & Bur- det 2005; Lauber & Wagner 2007) and on the type of environ- Linear mixed models with restricted maximum likelihood (REML) ment where they would be most likely to occur (‘snow bed’, were performed on both data sets from 2000 and from 2008 to test ‘wind edge’ or ‘neutral’; Aeschimann & Burdet 2005; Lauber & whether vegetation and soil characteristics differed between piste and Wagner 2007; Delarze & Gonseth 2008). The cover of each cate- off-piste plots, between machine-graded and ungraded plots, and gory was compared within pairs of plots. Biomass was harvested between artificial and natural snow. To account for the study design 3 cm above ground on two randomly chosen patches with pairs of plots on and next to pistes, we fitted the type of plot (20 · 20 cm) per plot and dried at 60 C for 48 h. Litter and pairs, i.e. the type of treatment of the piste plot in the pair. Because of woody shoots were removed to measure annual productivity. the frequent statistical significance of the interaction between the Plant nomenclature follows Lauber & Wagner (2007). effects of the ski piste and of machine-grading, the data set was split into graded pairs and ungraded pairs. This allowed us to analyse the impact of machine-grading and of ungraded ski pistes compared to SOIL DATA their control plots separately. We fitted the factor piste, which indi- In each plot, 10 samples of the topsoil (10 cm, mainly A horizon) were cated differences between plots on and next to pistes within the pairs, taken with a corer and pooled to obtain one representative soil sample across all types of piste treatment. Then we fitted the interactions of of c. 100g.Thesampleswerestoredat4C in a dark chamber for type of pair (duration of artificial snow production) and piste, which

Table 1. REML table presenting F-values and their significance in the differences within graded pairs of plots [Graded piste (GP) and off-piste plots (GO)], and interaction with duration of artificial snow production (DA) in the measured values for vegetation in 2008

F-values

Altitude Resort Diﬀ. GP ) GO Diﬀ. GP · DA ) GO · DA Source (d.f. = 1) (d.f. = 8) (d.f. = 1) (d.f. = 1)

% Vegetation cover 11Æ07** 0Æ52 34Æ72*** 0Æ54 Productivity 8Æ22* 1Æ19 13Æ07** 0Æ77 Species richness 2Æ42 0Æ78 3Æ17(*) 0Æ39 Shannon Index 2Æ30 0Æ62 3Æ53(*) 0Æ26 Humidity 3Æ03 0Æ59 1Æ78 8Æ47* Light 30Æ06*** 2Æ04 7Æ17* 0Æ18 Reactivity 4Æ12(*) 3Æ69(*) 6Æ72* 0Æ69 Nutrient 1Æ77 1Æ60 14Æ27** 0Æ58 Humus 2Æ82 1Æ35 5Æ72* 0Æ46 % Cov. grasses 0Æ02 0Æ65 16Æ05** 0Æ22 % Cov. legumes 0Æ05 1Æ47 0Æ35 1Æ54 % Cov. woody sp. 3Æ71(*) 2Æ00 4Æ69* 0Æ73 % Cov. forbs 0Æ98 1Æ05 3Æ27(*) 0Æ63 % Early ﬂowering 8Æ08* 0Æ84 11Æ97** 0Æ30 % Late ﬂowering 0Æ95 0Æ38 0Æ93 2Æ76 % Cov. snow bed sp. 9Æ11** 0Æ40 0Æ04 3Æ89(*) % Cov. ridge sp. 7Æ03* 1Æ25 0Æ13 0Æ08

(*)P <0Æ1; *P <0Æ05; **P <0Æ01; ***P <0Æ001.

2011 The Authors. Journal of Applied Ecology 2011 British Ecological Society, Journal of Applied Ecology, 48,906–915 Ski piste impacts on vegetation and soil 909 indicated whether the difference between plots on and next to the piste depended on the type of piste treatment. To test against the correct ***Diff. GP-GO error terms, the resort was tested as a random factor, and the type of 100 pair was nested within resort. Altitude was considered as a covariate and ski resort as a random factor in all analyses. For all analyses of 80 the vegetation and soil data of 2008, the use of artificial snow was treated as a continuous variable (named ‘Duration of artificial snow 60 production’) to test for possible cumulative effects over time. The machine-grading was treated as a binary factor, as it was applied only 40 once on a ski piste. Residuals of all variables were checked for nor- mality and homoscedasticity, and transformations were not neces- 20 Vegetation cover (%) sary. The statistical design followed the same approach as the one performed by Wipf et al. (2005). However, as some plots had changed 0 treatment meanwhile, which led to an unbalanced data set, we had to *Diff.GP-GO perform REML analyses instead of the ANOVA used earlier. We per- 40 GP GO UP UO formed the analyses on the data set from 2000 without obtaining any major changes from the result published in Wipf et al. (2005). 30 Although the Braun-Blanquet cover classes and Landolt indicator values do not fulfil all assumptions of continuous nor of ordinal variables, we decided to analyse them as continuous variables (mean per- 20 centage cover) to keep the design as comparable to Wipf et al. (2005) as possible. 10 To explore changes in the vegetation over time, we compared the Species richness effects of ski pistes in 2000 and 2008. For each plot pair and each dependent variable, we calculated the difference between piste and 0 off-piste plot for both years and analysed the change in pair-wise dif- 300 **Diff.GP-GO ferences in a similar way as above (Tables 4 and 5). Analysing the dif- GP GO UP UO ference between piste and control plot removed any potential 250 ) observer bias of the two independent sampling periods in 2000 and –2 2008. There were no significant vegetation changes observed on con- 200 trol plots. Therefore, a change over time in the difference between piste and control plot would be caused by changes on the ski pistes. 150 We tested whether differences between piste and off-piste plots dif- 100 fered between years and snow types (fitted as a categorical factor) for both graded and ungraded pairs. Because of the low replication and Productivity (g m 50 to avoid missing important ecological significances, we analysed P- values up to 0Æ1 and referred to these as being ‘marginally significant’. 0 Principal component analyses (PCAs) were used to evaluate the 4 correlation between soil and vegetation characteristics. Tests of Bart- *Diff.GP-GOGP GO UP UO lett and indices of Kaiser–Meyer–Olkin (KMO) showed that the variables could be factorized, and only those axes having eigenvalues >1 3 were taken into account (Kaiser’s law). REML analyses were performed using r 2Æ9Æ2 (R Development Core Team 2008), and spss 16Æ0 (SPSS 2007) was used to calculate the principal coordinate analyses. 2

Shannon index 1 Results

VEGETATION ON SKI PISTES 0 GP GO UP UO Grading of the ski slopes decreased total vegetation cover and the annual productivity (P <0Æ001 and P=0Æ003; Table 1 Fig. 1. Mean values of graded piste plots (GP), their control plots and Fig. 1). Mean number of species and Shannon diversity (GO) and of ungraded piste plots (UP) and their control plots (UO) for the measured vegetation parameters. The significant differences were marginally lower on graded ski pistes than on off-piste between piste plots and control plots are indicated [*P <0Æ05; plots (P =0Æ09 and P=0Æ08, respectively; Table 1 and **P <0Æ01; ***P <0Æ001]. Fig. 1). The species number decreased by approximately three species. Grading significantly affected indicator values by P=0Æ04; Table 1 and Fig. 2). Grading was responsible for favouring light, higher pH and nutrient demanding species and the decreased cover of early flowering species (P=0Æ004; by impeding humus indicating species (P=0Æ02, P=0Æ02, Table 1 and Fig. 2). P=0Æ001 and P=0Æ03, Table 1 and Fig. 2). The total cover Ungraded pistes (ski pistes in general) caused higher nutrient of graminoids and woody species decreased (P=0Æ001 and availability on ski pistes (P=0Æ04; Table 2 and Fig. 2).

2011 The Authors. Journal of Applied Ecology 2011 British Ecological Society, Journal of Applied Ecology, 48, 906–915 910 P. Roux-Fouillet, S. Wipf & C. Rixen

5 *Diff.GP-GO 4

-value 2 L

0 80 4 *Diff.GP-GO **Diff.GP-GO 60 3

-value 2 R

1 20 Grasses cover (%) Grasses cover 0 0 4 **Diff.GP-GO *Diff. UP-UO 50 *Diff.GP-GO

3 40 30

-value 2

N 20 1 10 Woody sp. cover (%) 0 0 *Diff.GP-GO **Diff.GP-GO 4 60

3 40

-value 2 H 20 1 Early fl. cover (%) cover fl. Early

0 0 GP GO UP UO GP GO UP UO

Fig. 2. Mean values of graded piste plots (GP), their control plots (GO) and of ungraded piste plots (UP) and their control plots (UO) for the Landolt indicator values for light (L-), reactivity (R-), nutrients (N-) and humus (H-value), the covers of two functional groups and of early flowering species. The significant differences between piste plots and control plots are indicated [*P <0Æ05; **P <0Æ01].

With increasing number of years of artificial snow produc- The PCAs calculated to test the link between impacts of ski tion on graded ski pistes, plant species that prefer moist condi- pistesonsoilsandonvegetationshowedapositivecorrelation tions were favoured (P =0Æ01; Table 1). Artificial snow between annual productivity and total vegetation cover production did not affect vegetation cover, productivity, spe- (r2 =0Æ472), which were both negatively correlated to soil cies richness or diversity. Finally, the interaction of grading bulk density (r2 = )0Æ436 and )0Æ571, respectively). Higher and artificial snow indicated a marginally significant decrease soil bulk density on pistes was associated with higher light indi- in the cover of snow bed species (P=0Æ067; Table 1). cator values (r2 =0Æ586). The PCAs further showed that the total N contents of the soils were positively correlated with vegetation cover (r2 =0Æ432) and productivity (r2 =0Æ364). The SOILS ON SKI PISTES soil pH-values were positively correlated with the indicator val- The grading of the slopes increased soil density by 56Æ6% ues for reactivity (r2 =0Æ608) and nutrient availability (P=0Æ002; Table 3 and Fig. 3) and caused significant (r2 =0Æ335), which in turn showed strong negative correla- changes in soil total nitrogen concentrations, showing lower tions with the humus value (r2 = )0Æ551 and )0Æ670, respec- values on graded pistes than on their control plots (P =0Æ003; tively). Table 3 and Fig. 3). Soil pH and C ⁄ N ratio significantly increasedongradedskipistes(P =0Æ01 and P =0Æ02; VEGETATION CHANGES ON SKI PISTES BETWEEN 2000 Table3andFig.3).Skipistesingeneralandartificialsnow AND 2008 production did not significantly affect the measured soil parameters. Soil Ptot analyses did not show any significant Several effects of grading were stronger in 2008 than they results and are not discussed further. appeared in 2000. Although we would expect a recovery of the

2011 The Authors. Journal of Applied Ecology 2011 British Ecological Society, Journal of Applied Ecology, 48,906–915 Ski piste impacts on vegetation and soil 911

Table 2. REML table presenting F-values and their significance in the differences within ungraded pairs of plots [Ungraded piste (UP) and off- piste plots (UO)], and interaction with duration of artificial snow production (DA) in the measured values for vegetation in 2008

F-values

Altitude Resort Diﬀ. UP ) UO Diﬀ. UP · DA ) UO · DA Source (d.f. = 1) (d.f. = 10) (d.f. = 1) (d.f. = 1)

% Vegetation cover 6Æ27* 4Æ27(*) 1Æ25 0Æ00 Productivity 0Æ66 1Æ18 2Æ99 0Æ89 Species richness 0Æ10 0Æ86 0Æ99 3Æ31 Shannon Index 0Æ31 0Æ66 0Æ70 1Æ57 Humidity 5Æ62* 0Æ59 0Æ02 1Æ89 Light 12Æ20** 5Æ62* 1Æ46 2Æ63 Reactivity 0Æ04 0Æ98 3Æ02 0Æ00 Nutrient 1Æ79 1Æ05 4Æ97* 0Æ50 Humus 0Æ53 1Æ75 2Æ55 0Æ05 % Cov. grasses 1Æ58 3Æ35(*) 0Æ07 1Æ71 % Cov. legumes 4Æ52* 0Æ69 0Æ37 0Æ10 % Cov. woody sp. 0Æ60 2Æ74 1Æ43 0Æ07 % Cov. forbs 0Æ01 1Æ01 0Æ12 4Æ49(*) % Early ﬂowering 1Æ34 1Æ40 0Æ20 0Æ45 % Late ﬂowering 0Æ03 1Æ19 0Æ08 0Æ05 % Cov. snow bed sp. 7Æ05* 0Æ48 0Æ85 0Æ14 % Cov. ridge sp. 14Æ58** 1Æ57 0Æ19 0Æ85

(*)P <0Æ1; *P <0Æ05; **P <0Æ01; ***P <0Æ001.

Table 3. REML table presenting F-values and their significance in the differences within graded pairs of plots [Graded piste (GP) and off-piste plots (GO)], and interaction with duration of artificial snow production (DA) in the measured values for soils in 2008

F-values

Altitude Resort Diﬀ. GP ) GO Diﬀ. GP · DA ) GO · DA Source (d.f. = 1) (d.f. = 8) (d.f. = 1) (d.f. = 1)

Ntot (%) 0Æ51 2Æ79 12Æ94** 0Æ01 Ptot (%) 0Æ12 1Æ13 0Æ95 1Æ41 pHKCl 0Æ05 2Æ93 7Æ75* 0Æ02 TOC (%) 1Æ13 2Æ62 1Æ20 0Æ04 C ⁄ N1Æ34 1Æ12 6Æ36* 0Æ06 Bulk density (g m)3)4Æ11 – 13Æ93** 0Æ28

(*)P <0Æ1; *P <0Æ05; **P <0Æ01; ***P <0Æ001. vegetation after time, the vegetation cover on graded ski pistes 2000 to 2008 (marginally significant for grass) (P =0Æ09 and was even lower in 2008 than in 2000, as indicated by an P =0Æ04; Table 5). increased difference between piste and off-piste plots (P =0Æ002; Table 4 and Fig. 4). The indicator value for reac- Discussion tivity also showed an increased difference in 2008, which points to an increased cover of species preferring high pH-values on This study demonstrates that machine-graded ski pistes, piste plots (P <0Æ006; Table 4 and Fig. 4). Graminoids ungraded ski pistes and artificial snow production induced spe- decreased in cover on the ski piste plots in 2008 compared with cific perturbations in the alpine vegetation and soils. We show 2000 (P =0Æ002; Table 4). that some impacts of ski runs were more pronounced in 2008 On ungraded ski pistes, the differences between piste and than 8 years earlier and that machine-graded pistes in particu- off-piste plots increased from 2000 to 2008 for the covers of lar show signs of even greater perturbation today than in the species indicating high base contents in the soil, showing previous survey. increased pH on ski pistes (P =0Æ01; Table 5 and Fig. 4). The The grading of ski slopes implies the use of heavy machin- cover of humus indicator species decreased on the ski piste ery, radical soil movement, removal of vegetation and topsoil. plots in 2008 compared with 2000 (P =0Æ07; Table 5 and Other fundamental factors that exert stress and disturbance on Fig. 4). graded and ungraded ski pistes include the mechanical impacts With artificial snow, the difference in the cover of grass and of skiers and snow-grooming vehicles. These types of distur- of forb species between piste and off-piste plots decreased from bances are likely to be responsible for changes such as the

2011 The Authors. Journal of Applied Ecology 2011 British Ecological Society, Journal of Applied Ecology, 48, 906–915 912 P. Roux-Fouillet, S. Wipf & C. Rixen

1·0 120 **Diff.GP-GO 2000 **Diff GP-GO × year 2008 0·8 100

0·6 80 (%) tot

N 0·4 60

0·2 40 Vegetation cover (%)

0·0 20 5 *Diff.GP-GO

0 4

2000 **Diff GP-GO × year 3 *Diff UP-UO × year KCl 2008 3

pH 2

1 2 -value

0 R *Diff.GP-GO 20 1

10 0 C/N ratio

5 2000 *Diff UP-UO × year 5 2008

4 0 ** Diff.GP-GO 3 ) 1·5 –3 -value

H 2 1·0

1 0·5 Bulk density (g m Bulk density

0 GP GO UP UO 0·0 GP GO UP UO Fig. 4. Mean values of graded piste plots (GP), their control plots (GO) and of ungraded piste plots (UP) and their control plots (UO) Fig. 3. Mean values of graded piste plots (GP), their control plots in 2000 and in 2008 for vegetation cover and Landolt indicator values (GO) and of ungraded piste plots (UP) and their control plots (UO) for reactivity (R-) and humus (H-value). The significant differences for the soil measurements. The significant differences between piste between piste plots and control plots are indicated [*P <0Æ05; plots and control plots are indicated [*P <0Æ05; **P <0Æ01]. **P <0Æ01]. greater proportion of un-vegetated ground, lower productivity rather limited by climatic conditions than by competition (which was correlated to decreased concentrations of soil (Kammer & Mo¨hl 2002). In our study, the suppression of organic nitrogen) and decreased species diversity on graded ski dominant species due to the disturbance caused by snow com- pistes. In alpine environments, plant diversity and richness are paction may therefore result in decreased vegetation cover and

2011 The Authors. Journal of Applied Ecology 2011 British Ecological Society, Journal of Applied Ecology, 48,906–915 Ski piste impacts on vegetation and soil 913

Table 4. F-values and their significance for the changes in the difference between graded piste (GP) and off-piste plots (GO) from 2000 to 2008 (Year) and respective changes on pistes with different snow types (ST)

F-values

Altitude Resort Diﬀ. GP ) GO · Year Diﬀ. GP ) GO · Year · ST Source (d.f. = 1) (d.f. = 8) (d.f. = 1) (d.f. = 1)

% Vegetation cover 2Æ79 1Æ30 15Æ22** 0Æ06 Productivity 0Æ91 0Æ75 0Æ25 0Æ68 Species richness 1Æ04 1Æ89 0Æ30 0Æ96 Shannon Index 0Æ70 1Æ83 0Æ48 0Æ01 Humidity 2Æ67 1Æ88 0Æ09 1Æ21 Light 0Æ38 0Æ52 0Æ43 0Æ01 Reactivity 0Æ24 0Æ28 11Æ28** 0Æ05 Nutrient 0Æ10 0Æ46 2Æ91 0Æ72 Humus 0Æ64 0Æ36 0Æ14 8Æ14* % Cov. grasses 8Æ29* 7Æ47* 15Æ57** 0Æ02 % Cov. legumes 5Æ84* 0Æ59 2Æ56 0Æ75 % Cov. woody sp. 0Æ35 1Æ54 0Æ33 2Æ18 % Cov. forbs 0Æ14 1Æ14 1Æ02 0Æ33 % Cov. early ﬂower 0Æ85 1Æ53 0Æ20 0Æ64 % Cov. late ﬂower 0Æ00 1Æ63 0Æ02 2Æ05 % Cov. snow bed sp. 0Æ21 1Æ63 0Æ07 0Æ15 % Cov. ridge sp. 3Æ01 0Æ58 1Æ58 0Æ03

(*)P <0Æ1; *P <0Æ05; **P <0Æ01; ***P <0Æ001.

Table 5. F-values and their significance for the changes in the difference between ungraded piste (UP) and off-piste plots (UO) from 2000 to 2008 (Year) and respective changes on pistes with different snow types (ST)

F-values

Altitude Resort Diﬀ. UP ) UO · Year Diﬀ. UP · UO · Year · ST Source (d.f. = 1) (d.f. = 8) (d.f. = 1) (d.f. = 1)

% Vegetation cover 1Æ49 1Æ00 1Æ00 0Æ49 Productivity 0Æ15 0Æ75 2Æ19 0Æ38 Species richness 1Æ03 0Æ51 0Æ02 0Æ13 Shannon Index 1Æ32 0Æ99 0Æ06 0Æ89 Humidity 1Æ36 0Æ37 1Æ19 0Æ36 Light 0Æ10 2Æ14 0Æ79 1Æ71 Reactivity 0Æ08 0Æ53 7Æ86* 0Æ55 Nutrient 0Æ86 0Æ69 2Æ74 0Æ29 Humus 0Æ46 0Æ42 3Æ8(*) 0Æ67 % Cov. grasses 0Æ46 0Æ97 0Æ43 3Æ25(*) % Cov. legumes 3Æ24(*) 1Æ52 1Æ48 0Æ52 % Cov. woody sp. 0Æ57 1Æ46 1Æ73 0Æ86 % Cov. forbs 0Æ48 1Æ78 0Æ02 5Æ04* % Cov. early ﬂower 2Æ66 1Æ40 0Æ02 2Æ13 % cov. late ﬂower 0Æ57 0Æ82 0Æ02 0Æ14 % Cov. snow bed sp. 0Æ79 0Æ59 0Æ06 0Æ53 % Cov. ridge sp. 1Æ59 2Æ64 0Æ01 0Æ16

(*)P <0Æ1; *P <0Æ05; **P <0Æ01; ***P <0Æ001. productivity instead of increased biodiversity (Kammer & them. Several factors may be responsible for the higher nutri- Mo¨hl 2002; Rixen 2002). ent availability, such as enhanced soil microbial activity in the The perturbation due to machine-grading was also reflected less acidic soils, high cover of legumes, or altered soil nutrient in the plant indicator values. The plant species indicated lower dynamics that are highly influenced by the soil temperature soil humus content, higher soil pH (confirmed by soil analysis) regime during winter (Grogan et al. 2004). In turn, these fac- and higher light availability on ski slopes, which can all be tors depend on snow pack characteristics (Brooks, Williams & explained by disturbance. Indicator values also pointed to Schmidt 1998; Rixen, Stoeckli & Ammann 2003; Freppaz et al. higher plant nutrient availability on ski pistes than next to 2007)andarethereforealsorevealedonungradedskipistes.

2011 The Authors. Journal of Applied Ecology 2011 British Ecological Society, Journal of Applied Ecology, 48, 906–915 914 P. Roux-Fouillet, S. Wipf & C. Rixen

The soil analysis on graded pistes, on the other hand, showed a cated by the significantly larger difference in vegetation cover low total nitrogen content, which is composed of up to 95% between piste and control plots over time. Although most organic nitrogen (Baize 2000), and an increased C ⁄ Nratio, other variables did not significantly change, there was no which usually indicates low biological activity in the soil (Baize indication for a succession towards a denser, more productive 2000). The combination of negative (disturbance) and benefi- or more diverse vegetation. The decrease in vegetation cover cial (i.e. higher soil pH) factors on ski pistes may cause the was especially pronounced on machine-graded pistes, which inconsistent nitrogen response.Furthermore,highnutrient indicates that the highly disturbed sites are subject to further and light indicator values are characteristic for pioneer com- deterioration. munities, which could be interpreted as an indication for a high degree of stress and disturbance (Grime 1979; Lacoste & Sal- Conclusion anon 2005). The disturbance on ski pistes is probably also responsible Our study demonstrates how ski pistes, particularly those that for the decrease in woody plants. The size and growth form of are machine-graded, exert disturbance changes both in vegeta- many alpine woody species (mostly of the Ericaceae family) tion and in soil characteristics. The grading of slopes had par- with buds and branches above ground level makes them partic- ticularly long-lasting negative impacts on vegetation cover and ularly vulnerable to mechanical damage, e.g. by snow-groom- soil density. Artificial snow production had minor effects; nev- ing vehicles (Ko¨rner 1999; Wipf et al. 2005). ertheless, the addition of water can change the vegetation com- The compaction of the snow cover and the subsequently position and should therefore be avoided in sensitive habitats decreased thermal insulation and colder soil temperatures (Ri- such as nutrient-poor and dry grasslands. xen 2002) could be responsible for the decrease of early flower- The negative impacts of machine-grading on vegetation ing species on ski pistes. Early flowering species usually cover had worsened 8 years after the previous assessment indi- develop their buds under the snow (Bilbourgh, Welker & Bow- cating poor recovery ability of these alpine sites after major dis- man 2000), but colder soils under compacted snow and longer turbance. As a consequence, machine-grading in alpine snow duration might hamper the development of early flower- habitats should be avoided or construction sites should be as ing species (Galen & Stanton 1995; Wipf et al. 2002, 2005; small as possible and re-vegetated with great care. Wipf, Rixen & Mulder 2006). This leads to slower and delayed phenological development and thus, a shorter time period for Acknowledgements reproduction. The dense soil on graded ski runs may be detrimental for We wish to thank J.-M. Gobat who contributed to this study through valuable comments and advice and who gave us access to the University of Neuchaˆ tel plant growth, as a negative relationship between soil density soil laboratories. We also thank M. Dawes for her support and advices in statis- and productivity was revealed in our results and has often been tics. We are grateful to the ski resorts managers and personnel for their collabo- demonstrated (Kozlowski 1999; Wallace 2004; De Paul & Bailly ration and support. Comments from two anonymous referees greatly helped to improve an earlier version of the manuscript. 2005). Moreover, the compacted soils of graded ski pistes can reduce infiltration rates and water-storage capacity, which in turn fosters fine soil erosion (Grismer & Hogan 2005; Martin References et al. in press). Surface erosion has been shown to be a factor Aeschimann, D. & Burdet, H.M. (2005) Flore de la Suisse et des territoires limi- causing seed loss by seed transportation (Urbanska & Fattorini trophes: le Nouveau Binz.Haupt.Berne. 1997). The slow re-vegetation and the lack of an establishment Amacher-Hoppler, A. & Schoch, R. (2008) Remonteés Mećaniques Suisses: Faits et Chiffres. Remonteés mećaniques Suisse, Berne. of a divers plant cover may cause a positive feed-back enhancing Baize, D. (2000) Guide des Analyses en Pe´dologie,2e` me e´dn. INRA, Paris. erosion rates (Pohl et al. 2009). Barni, E., Freppaz, M. & Siniscalco, C. (2007) Interaction between vegetation, On pistes with artificial snow, the significant increase in roots and soil stability in restored high-altitude ski runs in the Alps. Arctic, Antarctic and Alpine Research, 39, 25–33. moisture values of the vegetation is in line with the increased Bilbourgh, C.J., Welker, J.M. & Bowman, W.D. 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2011 The Authors. Journal of Applied Ecology 2011 British Ecological Society, Journal of Applied Ecology, 48, 906–915