1.	Download the following files from https://doi.org/10.1594/PANGAEA.946772
	## ABW_Mean_01_Jan (mean)
	## ABW_Qnt_1_01_Jan (10% quantile)
	## ABW_Qnt_9_01_Jan (90% quantile)
	## ...repeat for months 01 to 12	
	Download the following files from https://doi.org/10.1594/PANGAEA.946769
	## ABW_Mean_01_Jan (mean)
	## ABW_Qnt_1_01_Jan (10% quantile)
	## ABW_Qnt_9_01_Jan (90% quantile)
	## ...repeat for months 01 to 12	
	Download the following files from https://doi.org/10.1594/PANGAEA.946773
	## ABW_Mean_01_Jan (mean)
	## ABW_Qnt_1_01_Jan (10% quantile)
	## ABW_Qnt_9_01_Jan (90% quantile)
	## ...repeat for months 01 to 12	
	Download the following files from https://doi.org/10.1594/PANGAEA.946774
	## ABW_Mean_01_Jan (mean)
	## ABW_Qnt_1_01_Jan (10% quantile)
	## ABW_Qnt_9_01_Jan (90% quantile)
	## ...repeat for months 01 to 12	


2. Run stack_meansquant_reprocess.py to traverse all files and conduct the following steps:
	2a) Merge (1) mean, Q10, Q90 tifs into a single multiband tif.
	2b) Set band descriptions in all tifs (band1 = mean, band2 = Q10, band3 = Q90)
	2c) Reproject to EPSG:3031 and convert to COG


3. Run merge_allmonths.py to calculate a mean (of means) and mean Q10 & Q90 across all months, for the study period. i.e. generate a single aggregated habitat suitability map.


4. Run 04_merge_allmonths.py to create a single file for each species of the MEAN values for each month (12 bands, named with month name, each referenced by a different style) for Geoserver purposes.