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Agreement and uncertainty among climate change impact models: A synthesis of sagebrush steppe vegetation predictions

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Storage: The size of this resource is 411.5 MB
Created: Oct 02, 2019 at 9:10 p.m.
Last updated: Jun 10, 2020 at 6:45 p.m.
DOI: 10.4211/hs.3b420b738128411e8e1e11b38b83b5f1
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Content types: Geographic Feature Content  Geographic Raster Content 
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Ecologists have built numerous models to predict how climate change will impact vegetation, but these predictions are difficult to validate, making their utility for land management planning unclear. In the absence of direct validation, researchers can ask whether predictions from varying models are consistent. Here, we analyzed 43 models of climate change impacts on sagebrush (Artemisia tridentata Nutt.), cheatgrass (Bromus tectorum L.), pinyon-juniper (Pinus spp. and Juniperus spp.), and forage production on Bureau of Land Management (BLM) lands in the United States Intermountain West. These models consistently projected pinyon-juniper declines, forage production increases, and the potential for sagebrush increases in some regions of the Intermountain West. In contrast, models of cheatgrass did not predict consistent changes, making cheatgrass projections uncertain. While differences in emission scenarios had little influence on model projections, predictions from different modeling approaches were inconsistent in some cases. This model-choice uncertainty emphasizes the importance of comparisons such as this.

The projected vegetation changes have important management implications for agencies such as the BLM. Pinyon-juniper declines would reduce the BLM’s need to control pinyon-juniper encroachment, and increases in forage production could benefit livestock and wildlife populations in some regions. Sagebrush habitat may benefit where sagebrush is predicted to increase, but sagebrush conservation and restoration projects will be challenged in areas where climate may not remain hospitable. Projected vegetation changes may also interact with increasing future wildfire risk, potentially impacting vegetation and increasing management challenges related to fire.

Included in this page are the data and code used to complete this analysis and visualize results. This includes the original images of model results used in our analysis, and the code used to process and analyze these images to produce our final results.

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Resource Level Coverage


Coordinate System/Geographic Projection:
WGS 84 EPSG:4326
Coordinate Units:
Decimal degrees
Place/Area Name:
U.S. Intermountain West
North Latitude
East Longitude
South Latitude
West Longitude



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:

	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).

Additional Metadata

Name Value
Expected Results See Draft 1 of manuscript and figures in files "draft1_zimmer_et_al_2019.pdf"
Expected Reproducibility Level Artifacts available


Related Resources

This resource updates and replaces previous version: http://www.hydroshare.org/resource/6af3a8cc235d43a6a5be13298aee0af2
This resource has been replaced by newer version: http://www.hydroshare.org/resource/e6b15828d20843eab4e2babd89787f41


Funding Agencies

This resource was created using funding from the following sources:
Agency Name Award Title Award Number
The Wilderness Society
National Science Foundation Climate Adaptation Science 1633756

How to Cite

Zimmer, S., G. Grosklos, P. Adler, P. Belmont (2019). Agreement and uncertainty among climate change impact models: A synthesis of sagebrush steppe vegetation predictions, HydroShare, https://doi.org/10.4211/hs.3b420b738128411e8e1e11b38b83b5f1

This resource is shared under the Creative Commons Attribution CC BY.



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