Data format:        Raster grids*

Year:                  2001 and 2011

Spatial resolution: 250 m

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*Notes: The method uses forest polygon information from the first version of photoplots database from Canada’s National Forest Inventory as reference data, and the non-parametric k-nearest neighbors procedure (kNN) to create the raster maps of forest attributes. The approach uses a set of 20 predictive variables that include MODIS spectral reflectance data, as well as topographic and climate data. Estimates are carried out on target pixels across all Canada treed landmass that are stratified as either forest or non-forest with 25% forest cover used as a threshold. Within each pixel, the composition values of all tree species add to 100%.

White Spruce (Picea Glauca) distribution

Data format:        ESRI grids

Year:                  Interpolations of observed data, representative of 1960-1990*

Spatial resolution: 30 seconds (~1 km2)

*Notes: The newer Version 2.0 only contains current climate data. Although the climate data is more updated (1970-2000), it does not correspond with the future climate data used in projecting suitable habitat range for white spruce.

Bioclimatic data – Current 

Data format:       ESRI grids

Year:                 Downscaled global climate model data from CMIP5 (IPPC Fifth Assessment).                              The MRI-CGCM3 climate model projection was used in this analysis.

Spatial resolution: 30 seconds (~1 km2)

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Greenhouse gas scenarios (Meinhausen et al.,2011):

1. RCP 2.6: global annual GHG emissions (measured in CO2-equivalents) peak between                       2010–2020, with emissions declining substantially thereafter

2. RCP 4.5: emissions peak around 2040, then decline

3. RCP 6.0: emissions peak around 2080, then decline

4. RCP 8.5: emissions continue to rise throughout the 21st century

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Time periods: 
1.    2050 (average for 2041-2060) 
2.    2070 (average for 2061-2080)
 

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Bioclimatic data – Future projection