METHOD
Spatial coordinates of species occurrence data from over 55,000 weather stations across Western North America is inputted into the ClimateWNA software (Figure 4). This software generates climate variables based on spatial coordinates for multiple climate scenarios. This study used the normal 1961-1990 selection to generate climate data to represent a past dataset used to compare subsequent future scenario datasets. The future data was generated by selecting the Ensemble Mean for RCP 4.5 and RCP 8.5 for 2050 and 2080 timeframes for each. In total, 5 data sets were created using the ClimateWNA software. After ClimateWNA processes an output data file, data manipulation, visual representation, and quantification of tree species data takes place using R-software (RStudio Team, 2020). The creation of boxplots showing the range of values for each scenario side by side was used to visualize climate variables including mean annual temperature (MAT), mean annual precipitation (MAP), and the Hargreaves climatic moisture deficit (CMD). Further analysis through calculations to exclude the top 5 percent of values from Hargreaves climatic moisture deficit was conducted. This exclusion of far outliers accounts for the many factors that might account for drought resistance that are not included in this study. An increasing value indicates an increasing deficit of moisture available to the tree. By omitting those trees experiencing high moisture deficit within the past data set, we can generate greater certainty of the data. Maps are generated by layering species occurrence data on top of a Western North America shape file and ASC files from ClimateWNA that show elevation and climate variables for each scenario (Figure 5).
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CLIMATE SOFTWARE
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