Modeling Habitat Suitability based on Climate Projections

This lesson a cumulative application of the skills we developed in this course. We used data from the GESC to develop products from.

GIS Modeling of Climate Change Impacts on Q. garryana (a.k.a Oregon White Oak Garry Oak)

• Analyze potential future climate change impacts on forests in western North America
• Develop a GIS-based model to quantify projected shifts in the range of Q. garryana under various climate change scenarios for the late 21st century
• Test model and format it so that it will be easy to re-use it with other projections of future climate conditions

My full report can be found here.

The production of the Q. garryana model required advanced use of Raster Calculator expressions. It was very important that I understood the metadata of the underlying data and my early preparations were focused primarily on planning my processing steps carefully. I developed a historical climate envelope using Zonal Statistics As Table to find the mean of each climate variable plus or minus 1.5 standard deviations. I adjusted this range until the output for each climate variable appeared to match the known range of Q. garryana. I then used Raster Calculator to create binary output raster for each climate variable. These binary rasters were combined to create my final suitability maps. A model was created to repeat this process for each future climate projection. Lastly, I subtracted my 20th century suitability from the 2080 suitability to map the change in suitability.

The map series I created for this report can be seen below:

A model containing the input climate variables with their respective binary calculation and the final suitability output:

Each input is parameterized and can be rerun to include different datasets.