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.