Included on this page are the code and data used in our analysis "Agreement and uncertainty among climate change 
impact models: A synthesis of sagebrush steppe vegetation predictions"

The resources included in this repository are:

Contents
	draft1_zimmer_et_al_2019.pdf - first draft of final manuscript
	draft1_zimmer_et_al_2019_figures.pdf - first draft of final manuscript figures
  Analysis - Folder with data and code
	raw_images - folder with raw images of model results incoporated in our analysis
	georeferenced_rasters - folder with rasters of model results included in our analysis, after georeferencing
	classified_rasters - folder with rasters of model results included in our analysis, after georeferencing 
		             and unsupervised classification
	recoded_rasters - folder with rasters of model results included in our analysis, after georeferencing, 
		          unsupervised classification, and recoding values to values indicating increases, 
		          decreases, and no change in vegetation
	masked_rasters - folder with rasters of model results included in our analysis, after georeferencing, 
		          unsupervised classification, recoding values, and eliminating pixels not overlapping
			 BLM lands or the Intermountain West
	data - folder with additional data used in analysis
			renwick_supp.csv - CSV of supplemental results from Renwick et al 2018 used in analysis
			study_metadata.csv - CSV of important metadata in reference to the studies and individual 
					     models included in our analysis
			zonal_stats_results_masked -  folder of CSVs of results from each model, showing the number of
	                                              pixels indicating increases, decreases, or no change in vegetation
						      within each ecoregion of the Intermountain West
			gis - folder of gis layers used in analysis. These include BLM land, ecoregions and states
	scripts - folder of R scripts/code used in analysis
		analyze_renwick_results.R - takes the data from renwick_supp.csv and makes it analyzable similarly to 
				            the other rasters included in analysis
		final_analysis_and_figures.R - takes the data in the zonal_stats_results folder and analyzes it and
					       makes the figures included in manuscript
		mask_rasters.R - takes the recoded_rasters and eliminates areas not corresponding to Intermountain West
				 BLM lands
		recoding_rasters.R - recodes the values in the classified_rasters
		unsupervised_classification.R - takes the georeferenced_rasters and performs an unsupervised
						classification to identify similar pixels
		zonal_statistics.R - takes the masked_rasters and performs the zonal statistics analysis, counting the
 				     number of pixels showing increases, decreases, or no change within each ecoregion
				       		
The order of our analysis is:

1. Georeference the "raw_images". This was completed in ArcMap, so no script is available. 

2. Perform unsupervised classification on georeferenced_rasters to identifty similar pixel groups within images, using 
   the unsupervised_classification.R script

3. Recode the values of classified_rasters. Classification gives the pixel groups arbitrary values. Recoding the values
to make them meaningful is necessary. We recoded pixels corresponding to decreases in  vegetation as -1, pixels 
corresponding to increases as 1, pixels corresponding to no change as 0, and pixels not addressing vegetation 
(irrelevant background, legends, etc) as N/A. The recoding script is recoding_rasters.R

4. Mask the rasters. In this analysis, we were interested only in Intermountain West BLM lands, so pixels not 
overlapping BLM lands in the Intermountain West were removed by masking. The masking script is mask_rasters.R

5. Evaluate zonal statistics of masked rasters. For every masked raster, this counts the number of pixels indicating 
 vegetation increases, decreases, or no change within every ecoregion of the Intermountain West. The zonal statistics
 script is zonal_statistics.R

6. Using CSVs of zonal statistics results, data were analyzed and visualized. The final_analysis_and_figures.R 
   accomplishes this analysis

____

The results from Renwick et al. were supplemental results not provided as rasters. Therefore, they required some
analysis before they could be considered as other rasters were. The script analyze_renwick_results.R converts
these results from the original CSV of results (which are provided in the "data" folder) into rasters for
analysis. At the end of this script, they are at the end of step 3 (recoding).