Text S2 To derive community-inferred temperatures (CiT) in a “bottom-up” approach, we
fitted locally measured temperatures (LmT, the response variable) from soil miniature dataloggers against Ellenberg averaged values (EaV). We used the set of 138 vegetation plots
(Fig. S1a) for which we had information on LmT over a given time period (Table S1)
corresponding to the sampling period of the vegetation. We built a total of 14 models using
ordinary least-squares linear regressions to predict monthly mean temperature for each of the
12 months, annual mean temperature, and growing-season (June, July, August) mean
temperature. For each model, we randomly sampled, without replacement, half of the set of
vegetation plots available to calibrate the model. The remaining half was used to validate the
model and to assess its performances. This procedure was repeated 1,000 times for each of the
14 bottom-up models.
To derive CiT in a “top-down” approach, we fitted globally interpolated temperatures
(GiT, the response variable) from WorldClim against EaV. We first selected the set of
vegetation plots sampled during 1950-2000 (cf. WorldClim time period) and belonging to 1km2 climatic units that are relatively flat (e.g. mean roughness index less than 1.05) in order to
improve the quality of the fit between GiT and EaV. Then, we averaged EaV within each 1km2 climatic unit and selected the set of units having at least three vegetation plots to allow
for a reliable estimation of mean EaV within each 1-km2 climatic unit. A total of 121 1-km2
climatic units met this criterion (Fig. S1b). Similarly to the bottom-up modelling approach,
we built a total of 14 models using ordinary least-squares linear regressions to predict
monthly mean temperature for each of the 12 months, annual mean temperature, and growingseason (June, July, August) mean temperature. For each model, we randomly sampled,
without replacement, half of the set of 1-km2 climatic units available to calibrate the model.
The remaining half was used to validate the model and to assess its performances. This
procedure was repeated 1,000 times for each of the 14 top-down models.