mee312032-sup-0001-AppendixS1.zipZip archive7525KAppendix S1. Two R scripts and data are provided to reproduce the results of the paper.

Figure S1. Current distributions of the three species studied (Tutin et al. 1964–85; completed by Laurent et al. 2004).

Figure S2. Map of Europe indicating the frequency at which Pinus sylvestris is projected by STASH to be present or absent. The species is projected to be present in all 100 re-samplings for dark red pixels; in no re-samplings for dark blue pixels. Only a very small proportion of pixels, towards the margins of the projected distribution, are not consistently projected as either present or absent. The same applied to all three scenarios and all three species.

Figure S3. Binary STASH output for Pinus sylvestris. Red pixels indicate locations where the species is considered ‘present’ and blue pixels those where the species is absent. Overlaid black dots represent the Atlas Flora Europaea map for Pinus sylvestris.

Figure S4. Correlation circles of the principal components analysis for current climates, 2100 A1Fi scenario, 2100 B2 scenario and all climates together.

Figure S5. Coordinates of current (left column) and future (middle and right columns) climates in the principal components analysis. Axis 1 corresponds mostly to temperatures, with higher values denoting colder climates. Axis 2 corresponds to total precipitation, with higher values denoting wetter climates. Axis 3 is mostly carried by the seasonality of precipitations, with high values denoting regular amounts of precipitation across seasons.

Figure S6. ROC plot for the projection of Pinus sylvestris by LPJ.

Figure S7. Data subsets obtained for Pinus sylvestris.

Figure S8. Projected probability of occurrence for Pinus sylvestris.

Figure S9. Observed deviance (left) and modelled standardized deviance (right), for Pinus sylvestris.

Figure S10. Observed occurrence (top left), modelled occurrence (top right), observed (bottom left) and modelled deviances (bottom right). This figure is provided by line 87 of the ‘ConsensusModel.R’ script, using function CurrentPoccAndDevPic (Code: dataCurrent <- predSTDDevianceFunc(1, AkaikeweightsPinus, spec); CurrentPoccAndDevPic(spec)).

Figure S11. SDM outputs for the A1Fi scenario (period 2080–2100).

Figure S12. SDM outputs for the B2 scenario (period 2080–2100).

Figure S13. Projected occurrences and projected deviances for all climatic datasets. This image is generated by function ProjectedPoccAndDevPic.

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