Supporting information
A model-data comparison of Holocene timberline changes in the Swiss Alps
reveals past and future drivers of mountain forest dynamics
Christoph Schwörer1, Paul D. Henne1,2 and Willy Tinner1,2
1
Paleoecology, Institute of Plant Sciences and Oeschger Centre for Climate Change Research,
University of Bern, Switzerland
2
Forest Ecology, Institute of Terrestrial Ecosystems, ETH Zürich, Switzerland
LANDCLIM uses species-specific life history parameters (Tab. S1), site conditions (Fig. S1)
and climatic data to simulate tree growth, mortality and regeneration at each grid cell in
yearly time steps. The forest structure of each grid cell is modeled as tree age cohorts, i.e.
trees of the same species that have the same age. Yearly growth of the tree age cohorts is
determined by a species-specific maximum growth rate (Rmax), a maximum tree biomass
(Kmax) and three growth limiting factors (i.e. light availability, the sum of degree days and a
drought index). Light availability for each tree cohort is calculated by the model and reflects
competition, whereas the other two factors depend on climatic input data as well as the local
site conditions. Tree mortality is simulated by a stress-dependent, a density-dependent (i.e.
competition) and an intrinsic factor. The stress-depended mortality occurs when one of the
three growth-limiting factors drops below a threshold for a number of consecutive years.
Density-dependent mortality occurs when the carrying capacity of the forest stand is reached,
i.e. when simulated tree biomass is higher than the maximum stand biomass. Tree
regeneration depends on seed availability and local environmental conditions. The
establishment of new tree cohorts is only possible if light availability, winter temperature and
the sum of growing degree days are higher than the species-specific thresholds. Tree mortality
is also caused by landscape-scale disturbances such as windthrow and fire that are simulated
at decadal time steps. All the species-specific parameters used in LANDCLIM are based on data
from the literature and given in Table S1 (Schumacher et al. 2004, Henne et al. 2011). The
model can output the average biomass and stem counts of all tree species as t ha-1 in decadal
time steps for the entire landscape or a for a specified portion of the landscape. Additional
information about individual tree cohorts (e.g. age, biomass, height) in each cell can be
accessed for decades of interest.
Table 1: Life history parameters of all tree-species used in the LANDCLIM simulations (Schumacher et al. 2004, Henne et al. 2011).
Species
Maximum
age (yr)
Age at
maturity
(yr)
Effective
Max.
Vegetative
dispersal
dispersal
reproduction
distance (m) distance (m) probability
Max. age for
vegetative
reproduction (yr)
Rmax
(yr-1)
Kmax
(t)
Abies alba
700
70
50
160
0
0
0.08
17.6
Acer campestre
170
40
60
200
1
50
0.08
1.5
Acer platanoides
380
40
60
200
1
50
0.08
9.8
Acer pseudoplatanus
550
40
60
200
1
50
0.09
13.6
Alnus glutinosa
240
20
30
100
1
30
0.12
4.7
Alnus incana
150
20
30
100
1
50
0.12
4.9
Alnus viridis
100
20
30
100
1
50
0.09
0.035
Betula pendula
220
20
200
700
1
30
0.12
4.3
Betula pubescens
170
20
200
700
1
50
0.12
1.7
Carpinus betulus
220
30
55
180
1
30
0.1
4.6
Corylus avellana
70
10
30
-1
1
50
0.12
0.2
Fagus sylvatica
430
60
30
-1
1
50
0.1
28
Fraxinus excelsior
350
40
40
140
1
50
0.07
18
Ilex aquifolia
300
15
30
-1
1
50
0.05
3.57
Larix decidua
850
30
60
200
0
0
0.07
13.5
Picea abies
700
50
70
250
0
0
0.08
15.2
Pinus cembra
1050
70
30
-1
0
0
0.03
5.9
Pinus mugo
300
10
90
300
0
0
0.06
0.4
Pinus sylvestris
760
30
90
300
0
0
0.075
8.1
Populus tremula
140
20
240
800
1
50
0.12
3.8
Populus nigra
280
20
240
800
1
50
0.12
10.6
Quercus petraea
860
60
30
-1
1
50
0.11
26.4
Quercus pubescens
500
60
30
-1
1
50
0.07
2.4
Quercus robur
1060
60
30
-1
1
50
0.11
35.6
Salix alba
170
20
430
1400
1
30
0.1
5.7
Salix caprea
170
20
430
1400
1
50
0.12
0.4
Sorbus aria
180
10
30
-1
1
50
0.08
0.8
Sorbus aucuparia
110
10
30
-1
1
50
0.12
1.8
Tilia cordata
940
40
40
140
1
50
0.1
20
Tilia platyphyllos
960
40
40
140
1
50
0.1
45
Ulmus glabra
460
50
110
360
1
50
0.09
19
Max. dispersal distance: Species that are not wind-dispersed have no maximum dispersal distance, the values are therefore set to -1. Rmax:
Maximum above ground biomass growth rate. Kmax: Maximum aboveground biomass a species can potentially reach
Table 1 cont.
Species
Leaf type
Foliage
type
Min. Number
of Degree
Days
Min.
temperature
Drought
index
Drought
tolerance
Browsing
tolerance
Shade
tolerance
Fire
tolerance
Abies alba
Evergreen
5
641
-6
0.28
3
5
5
3
Acer campestre
Deciduous
2
1062
-99
0.3
3
2
3
2
Acer platanoides
Deciduous
3
1042
-17
0.3
3
4
3
2
Acer pseudoplatanus
Deciduous
3
898
-99
0.22
2
4
4
2
Alnus glutinosa
Deciduous
2
898
-16
0.13
1
1
3
2
Alnus incana
Deciduous
2
610
-99
0.13
1
1
2
2
Alnus viridis
Deciduous
2
272
-99
0.22
2
1
2
1
Betula pendula
Deciduous
1
610
-99
0.3
3
1
1
1
Betula pubescens
Deciduous
1
498
-99
0.13
1
1
1
1
Carpinus betulus
Deciduous
3
898
-9
0.3
3
2
1
1
Corylus avellana
Deciduous
3
898
-16
0.3
3
2
3
2
Fagus sylvatica
Deciduous
3
723
-4
0.3
3
3
5
1
Fraxinus excelsior
Deciduous
2
980
-17
0.13
1
3
3
2
Ilex aquifolia
Evergreen
5
1250
-2
0.28
3
3
4
3
Larix decidua
Deciduous
2
323
-11
0.38
4
3
1
5
Picea abies
Evergreen
5
385
-99
0.2
2
2
3
3
Pinus cembra
Evergreen
5
323
-99
0.28
3
4
3
4
Pinus mugo
Evergreen
4
323
-99
0.37
4
2
1
4
Pinus sylvestris
Evergreen
4
610
-99
0.42
5
3
1
4
Populus tremula
Deciduous
2
610
-99
0.3
3
2
2
3
Populus nigra
Deciduous
2
662
-99
0.13
1
2
3
3
Quercus petraea
Deciduous
3
785
-5
0.297
3
4
2
3
Quercus pubescens
Deciduous
3
1011
-99
0.46
5
4
2
3
Quercus robur
Deciduous
3
1042
-17
0.215
2
4
2
3
Salix alba
Deciduous
1
1062
-99
0.13
1
2
3
2
Salix caprea
Deciduous
1
610
-99
0.22
2
1
3
2
Sorbus aria
Deciduous
2
898
-99
0.38
4
4
2
2
Sorbus aucuparia
Deciduous
1
498
-99
0.38
4
4
2
2
Tilia cordata
Deciduous
3
1339
-19
0.38
4
2
3
2
Tilia platyphyllos
Deciduous
3
1339
-99
0.3
3
2
3
2
Ulmus glabra
Deciduous
3
1062
-16
0.215
2
3
4
3
Foliage type: Relationship between diameter and foliage weight. Min. temperature: Minimum mean temperature of the coldest month needed for
establishment. Set to -99 where no known values exist. Drought index: The value for which growth stops in the model. Derived from the drought
tolerance classes (see Henne et al. 2010). Drought tolerance:1 very intolerant to drought, 5 very tolerant. Browsing tolerance: 1 very tolerant to
browsing, 5 very intolerant. Shade tolerance: 1 very intolerant to shading, 5 very tolerant. Fire tolerance: 1 very tolerant to fire disturbance, 5 very
intolerant.
Table S2: Temperatures at Iffigsee (2065 m a.s.l.) at 11 000 cal. BP for the different
seasonality scenarios tested with the LANDCLIM model as well as the standard scenario. C is
the constant factor used to estimate monthly temperature anomalies based on monthly
insolation anomalies in the seasonality scenarios. Seasonality is the difference between the
coldest and the warmest month. GDD 5.5°C is the annual sum of growing degree days above
5.5 °C.
Scenario c
[Wm-2]
T warmest
month [°C]
T coldest
month [°C]
Annual T Seasonality
[°C]
[°C]
GDD
5.5°C
[d°C]
standard
-
8.9
- 6.2
1.0
15.1
495
Seas. 1
48
8.9
- 7.2
0.2
16.1
446
Seas. 2
36
8.9
- 7.5
0.0
16.4
434
Seas. 3
24
8.9
- 8.2
-0.6
17.1
408
Seas. 4
18
8.9
- 8.9
- 1.0
17.8
386
Seas. 5
12
8.9
- 10.2
- 2.1
19.1
353
Figure S1: Maps of different environmental input parameters used in the LANDCLIM
simulations. a) Elevation in m a.s.l. in the study landscape, b) slope in °, c) aspect, d) bucket
size estimated according to CTI ranging from 1 to 21 cm. Note that areas with bare rock or
above 2500 are not included in the simulations.
Figure S2: Growing degree days above 5.5 °C (GDD) at the elevation of Iffigsee (2065 m
a.s.l.) in the standard scenario (black curve) and the intermediate seasonality scenario (grey
curve).