SUPPORTING INFORMATION
Ap p en d i x S 1. S PO T satel l i te i mag ery
A 2006 SPOT 5 satellite image at 10 m resolution was obtained for the study area.
Following initial ortho-rectification and radiometric and geometric image correction using the
1B method (www.astrium-geo.com), a multilevel image segmentation was carried out in
eCognition Developer. Pixels were grouped into objects based on spectral similarity as defined
by four bands (green, red, near infrared and mid-infrared) as well as by the Normalized
Difference Vegetation Index (NDVI), which is derived from red and near infrared (Rouse et al.,
1974). Supervised object classification was performed using the following criteria: spectral
characteristics, spatial indices (e.g. form, area), texture (Haralick parameter) and context (e.g.
distance to other classes). Classification rules were calibrated using a 50-cm-resolution digital
colour infrared (CIR) photo for reference and adjusted to minimize omission (false negatives)
and commission (false positives) errors. Accuracy for the six classes (urban, bare ground,
meadow/heath, forest, glacier and water) was then visually assessed by comparing 257 random
control points to the CIR photo. Confusion matrices showed an overall accuracy among classes
of 91%. The resulting 10 m land cover map was exported at 100 m resolution using the majority
function to align with land cover and SDM outputs.
Ap p en d i x S 2. S p ecies d i stri b u ti on mod el l i n g
Model calibration
We used the National Alpine Botanical Conservatory (CBNA) vegetation database
together with presence information from the Swiss Floristic Network Center (CRSF) to obtain
occurrence data at 250 m resolution for a selection of 31 dominant plant species at the scale of
the French and Swiss Alps (Fig. 1C). Both species presence and absence information were
available based on field observations performed between 1980 and 2009 (see Boulangeat et al.,
2012 for details). We selected 10 tree species, 10 heath species and 11 alpine herb species to
represent mountain plants dominating species assemblages from montane, subalpine and alpine
vegetation belts (Table 1). Using 1950-2000 as a baseline period, we downscaled four Worldclim
environmental variables (Hijmans et al., 2005; annual growing degree days, summer moisture
index, summer solar radiation and annual minimum temperature) from 1 km to 250 m resolution
using the approach detailed in Zimmermann et al. (2007). Multivariate adaptive regression
splines (MARS), random forest (RF), maximum entropy (MAXENT), boosted regression trees
(GBM) and generalized linear models (GLM) algorithms were used to statistically relate plant
occurrence to environmental variables at 250 m resolution at the scale of the French and Swiss
Alps. Each algorithm was calibrated for the baseline period using a 70% random sample of the
initial data and was evaluated against the remaining 30% using the True Skill Statistic (TSS,
Allouche et al., 2006). The whole procedure was repeated 10 times, thus providing a 10-fold
cross-validation. Each calibrated model was then projected under current and future conditions to
the Chamonix area (see Model Prediction section). To do so, we applied an ensemble forecasting
technique that used the mean of projections from all algorithms and cross-validations weighted
by their respective predictive performance (Marmion et al., 2009; see Fig. S1 for model
performance by species). Only models for which the TSS was higher than 0.3 were kept in the
final ensemble forecast. The ensemble forecast was transformed into presence–absence maps
using the threshold that maximizes TSS. Species distribution modelling was performed using the
biomod2 package in R (Thuiller, 2009).
Model prediction
Climate predictions for 2021-2050 and for 2051-2080 were obtained from the RCA3
regional climate model (Samuelsson et al., 2010) driven by the ECHAM5 global circulation
model, based on the A1B climate scenario (IPCC 2000). Future climate was downscaled to the
100 m resolution by means of the change factor method (Anandhi et al., 2011). Initial
environmental variables were derived from temperature and precipitation grids at the scale of the
Mont Blanc range using a digital elevation model and lapse rate interpolation methods
established by Zimmermann & Kienast (1999) and further detailed in Randin et al. (2009).
SDMs were projected to the Chamonix Valley study area at 100 m resolution in alignment with
the land cover analysis, and annual model predictions were averaged for the periods 2021-2050
and 2051-2080.
Ap p en d i x S 3. L and cover mod el s el ect i on
Performance of forestation index (FI) models (as estimated by AIC, log likelihood and
Pearson’s r) improved at coarser resolutions (Table S1). The random effects residuals also
diminished as grid cell size increased, suggesting that explanatory variables were able to account
for less of the variation in between grid cell forest cover at finer resolutions. Binary forest maps
exhibited greater forest expansion in 2021-2050 and in 2051-2080 when derived from FI models
at coarser resolutions, owing to the fact that larger grid cell sizes allowed historical trends to be
extrapolated across broader spatial areas (Table S2). Predicted forest cover resulting from the
300 m resolution FI model was retained for integration into the dynamic land cover map for two
reasons: (1) given that observed changes in tree line between 1952 and 2008 occurred on the
scale of 100 to 300 m of expansion (Fig. 3C), this grid cell size allowed for visualization of
comparable magnitude of change when predicting future forest extent and (2) the 300 m
resolution provided a compromise between enhanced model performance at coarser resolutions
and improved spatial precision at finer resolutions. Concerning the Glacier Index (GI), the choice
of grid cell size was not imposed as resolutions above 100 m were too coarse to represent ice
extent. Although the high resolution (5 m) of historical forest and glacier layers would have
enabled modelling at resolutions finer than 100 m, this approach would have potentially underestimated future land cover change by restricting dynamic zones to fringe areas. This
phenomenon is illustrated in Table S2, which shows that predicted range change decreased as
grid cell size became finer.
Figure S1. Model performance, estimated by the true skill statistic (TSS), by vegetation group.
Figure S2. Glacier retreat and plant succession on the Mer de Glace Glacier between 1952 and
2008.
Forest (FI)
Glacier (GI)
Grid Cell
Resolution
AIC
logLik
100 m
200 m
300 m
400 m
500 m
100 m
65157
13503
5222
2594
1507
53584
-32569
-6743
-2602
-1288
-744
-26782
Random
Effects
Residual
0.27
0.25
0.23
0.22
0.21
0.18
Pearson's r
0.94
0.95
0.95
0.96
0.96
0.98
Table S1. Model evaluation parameters for forest and glacier linear mixed models relative to grid cell
resolution. Akaike’s information criteria (AIC), log likelihood and random effects residuals were used to
assess the ability of fixed effects to explain between grid cell variation in FI and GI. Resolutions that
appear in bold were retained for forest index (FI) and glacier index (GI) modelling.
Grid cell
% Loss % Gain Loss Gain
resolution
100 m
1.29
51.21
65 2580
200 m
3.35
60.89
169 3068
2021-2050 300 m
3.10
71.73
155 3588
400 m
4.30
71.67
215 3585
500 m
4.81
77.27
238 3824
100 m
0.62
59.67
31 3006
200 m
1.21
70.17
61 3536
2051-2080 300 m
1.48
82.97
74 4150
400 m
2.14
86.29
107 4316
500 m
2.20
94.02
109 4653
Area (ha)
Stable
Stable
absence
presence
13400
4973
13222
4870
12640
4847
12642
4787
12329
4711
13136
5007
12754
4978
12078
4928
11911
4895
11500
4840
Range
change
50
58
69
67
72
59
69
81
84
92
Table S2. Range change metrics for predicted forest cover in 2021-2050 and in 2051-2080 for
different grid cell resolutions. The 300 m resolution is in bold as this was the grid cell size that
was retained and integrated into the dynamic land cover filter.