Supplementary information for the paper "Alternative Tree-Cover States of the Boreal Ecosystem: a Conceptual Model" by B. Abis and V. Brovkin, 2018.

All the datasets used are included in the Supplementary_Python folder. 
All the scripts for finding the competition coefficients and reproducing the plots are in the Supplementary_Python folder as Ipython notebooks. 
All data and functions to load for python are in the Supplementary_Python/Files_to_load_and_import_Modis and Datasets folders.
In general, the scripts are meant as Ipython notebooks, but python versions can also be provided. 
EA stands for Eurasia, NA for North America, E for East, W for West, and numbers refer to the regional division presented in the paper.

CDO 1.7.1 and NCO 4.4.8 were used for most of the preprocessing of the original datasets.
All environmental variables datasets are as in Abis, B. and Brovkin, V. - Environmental conditions for alternative tree-cover states, Biogeosciences (2017), which is an open access paper and contains all information on datasets elaboration in the supplementary. 
The same analysis has been performed here using Landsat data in the notebook 02_Paper_Dataset_Filtering_Boreal_4_Regions_with_Cru_Landsat.ipynb. 
Data necessary to run it are also included in the Datasets/Cru_filtering folder. Command history is included in the meta-data of each netcdf file in Datasets/Cru_filtering. 
The rest of the IPython notebooks created with Python 2.7.10 and IPython 4.0.1.

Model simulations were performed using Wolfram Mathematica 11.0.1.0. All scripts are included in the folder Supplementary_Mathematica.

The analysis of the dynamical system to find the number of equilibria and their stability was performed in Maple 2015.0. All Maple scripts are included in the folder Supplementary_Maple.


Python scripts contain paths to datasets which are relative to the script location and should not be updated unless specified in the script. The scripts are numbered and contain the following:
0 script to find the competition coefficients of the case Larix vs Picea. Changing indexes in the script allows to reproduce computation for every other combination of species. A legend of the indexes is provided int he script; (using MODIS data)
1 analysis of the greening trends datasets together with multistable areas;
2 preprocessing of datasets using LANDSAT and CRU data, for comparison; 
3--6 plots for multistable areas using MODIS and LANDSAT, histograms with model results, the division of multistable areas, and examples of the evolution of different species in North America and Eurasia;
7 script to find the competition coefficients of the case Larix vs Picea. Changing indexes in the script allows to reproduce computation for every other combination of species. A legend of the indexes is provided int he script; (using LANDSAT and CRU data)
8 dependance of the model parameters on the permafrost variable.

Mathematica scripts contain paths to datasets which should be manually updated as instructed in the scripts. They are subdivided into two folders, one for LANDSAT results and one for MODIS (paper plots are based on MODIS results). They can be adapted to run with every combination of species. The scripts are numbered and contain the following:

LANDSAT:
0 simulations in Eurasia using Larix and Picea;
1 simulations in Eurasia using Larix and Abies;
2 simulations in Eurasia using Pinus and Abies;
3 simulations in North America using Pinus and Picea;
4 simulations in North America using Pinus and Picea.
 
MODIS:
0 simulations in Eurasia using Larix and Picea;
1 simulations in Eurasia using Larix and Abies;
2 simulations in North America East 1 using Pinus and Picea;
3 simulations in North America East 2 using Pinus and Picea;
4 simulations in North America West using Pinus and Picea.


Original data and analysis for multistable regions: 
https://www.biogeosciences.net/14/511/2017/bg-14-511-2017.html 


Python packages installed are as follows:
python: stable 2.7.12
basemap (1.0.7)
brewer2mpl (1.4.1)
cdo (1.2.6)
ipykernel (4.2.0)
ipython (4.0.1)
ipython-genutils (0.1.0)
ipywidgets (4.1.1)
jupyter (1.0.0)
jupyter-client (4.1.1)
jupyter-console (4.0.3)
jupyter-core (4.0.6)
matplotlib (1.5.0)
nco (0.0.2)
netCDF4 (1.2.1)
nose (1.3.7)
notebook (4.0.6)
numpy (1.10.1)
palettable (2.1.1)
pandas (0.17.1)
path.py (8.1.2)
pickleshare (0.5)
Pillow (2.7.0)
py (1.4.31)
rpy2 (2.7.4)
scikit-learn (0.17)
scipy (0.16.1)
seaborn (0.6.0)
setuptools (18.7.1)
sklearn (0.0)
snakeviz (0.4.0)
sympy (0.7.6.1)