# The Influence of Wild Ungulates on Forest Regeneration in an Alpine National Park

^{1}

^{2}

^{3}

^{*}

## Abstract

**:**

## 1. Introduction

^{2}in our study area; see Methods section). In addition to red deer, Alpine ibex (Capra ibex), Alpine chamois (Rupicapra rupicapra), and roe deer (Capreolus capreolus; at low densities) occur in the study area.

## 2. Methods

#### 2.1. Study Area

^{2}, Val Trupchun represents almost one-eighth of the area of the SNP. It covers elevations of 1800 to 2800 m a.s.l. and is characterized by an inner-alpine dry climate. Mean (±SD) air temperatures are 11.5 °C ± 3.0 °C in summer and −6.1 °C ± 5.0 °C in winter, with an annual mean precipitation of 695.5 ± 120.6 mm (as measured at the weather station in Samedan, at 1708 m a.s.l., between 2012 and 2021 by MeteoSwiss [20]). The two slopes of Val Trupchun differ in their climatic conditions and past land use. The northeast-exposed slope was used for grazing until 1960 and was added to the protected area of the SNP in 1961. This slope lies in the shadow of the mountain flank. By contrast, the southwestern slope is exposed to more solar radiation and has been part of the SNP since its foundation in 1914 [21].

^{2}, followed by Alpine ibex at 7 individuals/km

^{2}and Alpine chamois at 6 individuals/km

^{2}in 2021 (ungulate observation data from the Swiss National Park, 2021; Supplementary Material, Figure S1. For a description of methodology, see Anderwald et al. [23]). Apart from these three species, there are a small number of roe deer in Val Trupchun. Red deer undertake major seasonal migrations: in summer, they stay in Val Trupchun; in autumn, they migrate out of the valley to spend the winter in surrounding areas at lower elevations [13].

#### 2.2. Sampling Design

#### 2.3. Development of the Numbers of Trees over Time

- Development of the numbers of saplings and young trees of the five most common tree species between 1991 and 2021;
- Development of the numbers of saplings and young trees of the five most common tree species between 1991 and 2021 on the two slopes of Val Trupchun;
- Development of the numbers of individuals within each developmental stage per tree species between 1991 and 2021.

#### 2.4. Factors Possibly Affecting the Probability of Browsing

^{®}ArcMap

^{TM}from the program ArcGIS Desktop, version 10.8.0.12790, Redlands, California. We calculated topographic values using a digital elevation model with a grid size of 2 m, created and provided by the SNP. Slope and aspect were calculated with the Surface Tool of the Spatial Analyst Extension. We changed the unit of aspect from degree to rad and calculated the sin of the resulting values to obtain a measure for eastness, representing the east–west gradient (with east-exposed sampling plots = 1, and west-exposed sampling plots = −1). Northness, representing the north–south gradient (with north-exposed sampling plots = 1, and south-exposed sampling plots = −1), was calculated by applying the cos function to the same values.

- $S$: total number of tree species within saplings of the plot;
- $i$: tree species of the saplings;
- $N$: total number of saplings of the five tree species within the plot;
- ${n}_{i}$: number of saplings belonging to tree species $i$ within the plot.

**Table 4.**Predictors of the probability of browsing and the correlation expected a priori, as well as the reasoning behind that expectation.

Predictor | Correlation Expected a priori | Reason |
---|---|---|

Cembra pine | Least browsed | From the literature, we know that wild ungulates have different preferences for spruce and larch [31]. |

Spruce | Medium browsed | |

Larch | Most browsed | |

Developmental stage | Unclear | Wild ungulates might prefer some developmental stages over others [32]. |

Topography | ||

Elevation | Positive [14] | Meadows, which are the preferred foraging grounds of wild ungulates, are mostly at high elevations above the tree line. |

Eastness | Unclear | Sampling plots are differently exposed to wind due to their east-west exposition. |

Northness | Positive | South-exposed sampling plots are exposed to high solar radiation, meaning challenging conditions for ungulates in summer [33]. |

Slope | Negative | Red deer, the ungulate species with the highest density in Val Trupchun, are better adapted to flatter terrain [34]. |

Location factors for 4 m sampling plot | ||

Total number of saplings | Positive | A high number of saplings represent extensive foraging grounds for wild ungulates. |

Average developmental stage | Unclear | The developmental stage of the surrounding saplings may have an influence on the browsing probability [35]. |

Shannon index | Positive | A high diversity of saplings may represent a resource of different nutrients and minerals. |

Distance to next hiking trail | Positive | Wild ungulates avoid human presence. |

Distance to next meadow | Unclear | Meadows are the main and preferred foraging grounds of wild ungulates [18]. |

## 3. Results

#### 3.1. Development of the Numbers of Trees over Time

#### 3.1.1. Overall Development of the Number of Trees

#### 3.1.2. Differences between Opposite Slopes of the Valley

#### 3.1.3. Differences between Developmental Stages

#### 3.2. Factors Affecting the Probability of Browsing

## 4. Discussion

#### 4.1. Development of the Numbers of Trees over Time

#### 4.1.1. Overall Development of the Numbers of Trees

^{2}is very high compared to other regions in Switzerland and neighboring countries [13] and has remained consistently high in the last decades (ungulate observation data from the Swiss National Park, 2021; Figure S1). Nonetheless, the increasing numbers of saplings and young trees per sampling plot over the last 30 years (Figure 3) suggests that the potential of the forest to regenerate has increased despite consistently high densities of wild ungulates. This confirms the findings of Weppler and Suter [39], who investigated forest regeneration in Val Trupchun between 1991 and 2003, and of Brüllhardt et al. [40], who investigated forest regeneration between Val Trupchun and Il Fuorn in 2011. However, we do not have a control area with similar topographic and climatic conditions but without wild ungulates; therefore, the absolute impact of wild ungulates on forest regeneration remains unclear. Nevertheless, an exclusion experiment in Val Trupchun by Camenisch and Schütz [41] found that there were no obvious divergent trends in forest regeneration between enclosures and control areas. Therefore, our results are consistent with earlier studies in Val Trupchun, suggesting that the forest in this valley is able to regenerate despite the high density of wild ungulates. Indeed, the forest of Val Trupchun may still have the potential to expand because the northeast-exposed slope of the valley was used for grazing until 1960, and the forest of the whole valley shows evidence of logging up to the 19th century [21].

#### 4.1.2. Differences between Opposite Slopes of the Valley

#### 4.1.3. Differences between Developmental Stages

#### 4.2. Factors Affecting the Probability of Browsing

## 5. Conclusions

## Supplementary Materials

## Author Contributions

## Funding

## Data Availability Statement

## Acknowledgments

## Conflicts of Interest

## References

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**Figure 1.**Study area in the Swiss National Park. The black line is the border of Switzerland, the red line is the border of the Swiss National Park, and the yellow dots are sampling plots used in this study. Background maps ©swisstopo.

**Figure 2.**Adjustment of a horizontal circle to a slope gives an ellipse. For more convenient data collection in the field, the ellipse was converted to a circular area. The slope (α) was measured in degrees, and the adjusted radius of the circular area was calculated using the following formula by Kramer and Akça [25]: r

_{adjusted}= $r/\surd (\mathrm{cos}\alpha )$.

**Figure 3.**The number of saplings (

**left**) and of young trees (

**right**). The left y-axis represents the count data, and the right y-axis the count data extrapolated to the hectare. Saplings include developmental stages 1 to 6, and young trees include developmental stages 7 to 9 (Table 2). Data are from n = 168 sampling plots that were sampled in each sampling year. Boxes show medians and interquartile ranges, whiskers are observations within 1.5 times the interquartile range.

**Figure 4.**The number of saplings (

**left**) and young trees (

**right**) on the northeast-exposed (blue) and southwest-exposed slope (brown) of Val Trupchun. The left y-axis represents the count data, and the right y-axis the count data extrapolated to the hectare. Saplings include developmental stages 1 to 6, and young trees include developmental stages 7 to 9 (Table 2). Data are from sampling plots that were sampled in each sampling year (northeast-exposed slope: n = 108 plots, southwest-exposed slope: n = 60 plots). Boxes show medians and interquartile ranges, whiskers are observations within 1.5 times the interquartile range, circles are outliers.

**Figure 5.**The mean number of saplings and young trees combined per hectare according to developmental stage (Table 2) and sampling year. Data are from n = 168 sampling plots that were sampled in each sampling year. Saplings from developmental stages 5 and 6 in 1991 were combined into one developmental stage 5.5, represented by the dashed line. Further, data on developmental stages 7, 8, and 9 in the year 1991 were not included (see Table 3).

**Figure 6.**(

**a**): Probability of browsing of saplings of cembra pine (green), spruce (brown), and larch (golden) in developmental stage 3 (point estimates for the other developmental stages would be parallel according to (

**b**)). (

**b**): Probability of browsing of larch in developmental stages 2 to 6 (estimates of the other tree species would be parallel according to (

**a**)). Black lines are 95% compatibility intervals [38].

**Figure 7.**The probability of browsing of saplings for each topographic and location predictor, based on the binomial generalized linear mixed-effects model. Dots represent the proportion of browsed saplings of the same tree species in the same developmental stage per sampling plot and sampling year (n = 1808 dots). Shaded areas are 95% compatibility intervals of the probability of browsing for larches in developmental stage 3, taking the other predictors into account. The point estimates and compatibility intervals of cembra pine and spruce and their developmental stages would be parallel at different heights according to Figure 6a,b.

Year | Total Number of Plots Sampled | Consecutive Plots ^{1} from All Previous Years | ||||
---|---|---|---|---|---|---|

Plots on Northeast-Exposed Slope | Plots on Southwest-Exposed Slope | Total | Plots on Northeast-Exposed Slope | Plots on Southwest-Exposed Slope | Total | |

1991 | 153 | 74 | 227 | 153 | 74 | 227 |

2003 | 122 | 83 | 205 | 120 | 67 | 187 |

2011 | 120 | 87 | 207 | 111 | 66 | 177 |

2021 | 123 | 79 | 202 | 108 | 60 | 168 |

^{1}Plots that were surveyed in all previous sampling years.

Developmental Stages | |
---|---|

Saplings | Height |

1 | Germ bud |

2 | −9.99 cm |

3 | 10–39.99 cm |

4 | 40–69.99 cm |

5 | 70–99.99 cm |

6 | 100–129.99 cm |

Young trees (>130 cm height) | Breast-height diameter |

7 | −7.99 cm |

8 | 8–15.99 cm |

9 | 16–24 cm |

Developmental Stages | ||

Standardized Method | 1991 | Dealing with difference |

See Table 2 | Developmental stages 5 and 6 were combined into one developmental stage | Visualization of a developmental stage 5.5 that includes the number of trees from developmental stages 5 and 6 |

Count data of young trees | ||

Standardized Method | 1991 | Dealing with difference |

Young trees and their trunk damage were assessed within an 8 m radius | Young trees and their trunk damage were assessed within a 4 m radius | Exclusion of the data on young trees and trunk damage collected in 1991 |

Slope correction | ||

Standardized Method | 1991 | Dealing with difference |

Adaption of the radius to the slope to obtain a consistent horizontal radius of 4 m or 8 m | No slope correction | Extrapolation of the counted number of trees to the radius that is slope-corrected |

**Table 5.**Transformations of the predictors of the binomial generalized linear mixed-effects model. For scaling, the values were divided by the standard deviation (given in Table 6), so that the standard deviation of the resulting variable was 1.

Predictor | Data Type | Unit | Transformation |
---|---|---|---|

Tree species | Factor | - | |

Developmental stage | Ordered factor | - | |

Topography | |||

Elevation | Numeric | m a.s.l. | Centered and scaled |

Eastness | Numeric | Centered and scaled | |

Northness | Numeric | Centered and scaled | |

Slope | Numeric | % | Centered and scaled |

Location factors for 4 m sampling plot | |||

Total number of saplings | Numeric | log-transformed, centered and scaled | |

Average developmental stage | Numeric | Centered and scaled | |

Shannon index | Numeric | Centered and scaled | |

Distance to next hiking trail | Numeric | m | log-transformed, centered and scaled |

Distance to next meadow | Numeric | m | log(+1)-transformed, centered and scaled |

**Table 6.**Parameter estimates of the mixed binomial model for browsing probability. The suffix “.z” of the variables indicates that they were centered and scaled, and transformations are indicated by their function. For the scaled variables, the standard deviation of the variable before scaling is indicated in squared brackets. Means and 2.5% and 97.5% quantiles are based on 2000 samples drawn from the joint posterior distribution. The sample size is n = 1808 data points (browsed saplings of the same tree species in the same developmental stage per sampling plot and sampling year).

Fixed Effects | |||

95% Compatibility Interval | |||

Explanatory Variable | Mean | 2.5% Quantile | 97.5% Quantile |

Intercept (developmental stage 2 of cembra pine) | −5.13 | −5.86 | −4.44 |

Spruce | 1.53 | 0.93 | 2.15 |

Larch | 2.15 | 1.82 | 2.50 |

Developmental stage 3 | 1.19 | 0.69 | 1.66 |

Developmental stage 4 | 0.87 | 0.35 | 1.42 |

Developmental stage 5 | 1.02 | 0.46 | 1.59 |

Developmental stage 6 | 0.41 | −0.22 | 1.049 |

log(Number of saplings).z [0.98] | −0.074 | −0.23 | 0.077 |

Shannon index.z [0.31] | −0.17 | −0.34 | 0.0045 |

Average developmental stage.z [0.86] | −0.074 | −0.25 | 0.091 |

Elevation.z [91 m] | 0.51 | 0.21 | 0.80 |

Slope.z [14.6%] | −0.10 | −0.28 | 0.085 |

log(Dist. to next hiking trail).z [1.1 ln(meter)] | 0.16 | −0.11 | 0.44 |

log(Dist. to next meadow + 1).z [1.6 ln(meter)] | −0.064 | −0.25 | 0.12 |

Eastness.z [0.48] | 0.24 | 0.075 | 0.40 |

Northness.z [0.57] | −0.34 | −0.54 | −0.14 |

Random Effects | |||

Variables | Groups | Variance | Standard Deviation |

Plot ID | 197 | 0.44 | 0.67 |

Year | 4 | 0.18 | 0.42 |

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**MDPI and ACS Style**

Fluri, J.; Anderwald, P.; Korner-Nievergelt, F.; Wipf, S.; Amrhein, V.
The Influence of Wild Ungulates on Forest Regeneration in an Alpine National Park. *Forests* **2023**, *14*, 1272.
https://doi.org/10.3390/f14061272

**AMA Style**

Fluri J, Anderwald P, Korner-Nievergelt F, Wipf S, Amrhein V.
The Influence of Wild Ungulates on Forest Regeneration in an Alpine National Park. *Forests*. 2023; 14(6):1272.
https://doi.org/10.3390/f14061272

**Chicago/Turabian Style**

Fluri, Jeannine, Pia Anderwald, Fränzi Korner-Nievergelt, Sonja Wipf, and Valentin Amrhein.
2023. "The Influence of Wild Ungulates on Forest Regeneration in an Alpine National Park" *Forests* 14, no. 6: 1272.
https://doi.org/10.3390/f14061272