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Agreement and uncertainty among climate change impact models: A synthesis of sagebrush steppe vegetation predictions
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|Created:||Oct 02, 2019 at 9:10 p.m.|
|Last updated:|| Jun 10, 2020 at 6:45 p.m.
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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.
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).
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|Expected Results||See Draft 1 of manuscript and figures in files "draft1_zimmer_et_al_2019.pdf"|
|Expected Reproducibility Level||Artifacts available|
|This resource updates and replaces a previous version||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.6af3a8cc235d43a6a5be13298aee0af2|
|This resource has been replaced by a newer version||Zimmer, S., G. Grosklos, P. Belmont, P. Adler (2020). Agreement and uncertainty among climate change impact models: A synthesis of sagebrush steppe vegetation predictions, HydroShare, https://doi.org/10.4211/hs.e6b15828d20843eab4e2babd89787f41|
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|