Habitat Suitability - Project on the Rocky Mountain Juniper

(Juniperus scopulorum Sargent) in Colorado

DOI :

Project Description

Create a habitat suitability model for Juniperus scopulorum Sargent, a tree native to the Rocky Mounatin Region of North America. This habitat suitability model will focus on creating a modular, reproducible workflow. This project will choose two study areas - National Grasslands, one in southern Colorado - Comanche National Grassland, and one in Nothern Colorado - Pawnee National Grassland. Both of these National Grasslands are in the Eastern Plains of Colorado which are to the east of the Rocky Mountains. The model used will be based on combining multiple data layers related to soil, topography, and climate (as raster layers) within the study area envelope (the two grasslands chosen).

Two variables related to soil are chosen: pH and theta_s (saturated soil water content)

Two climate models are chosen:

Steps on project

1. Define study area: Download the USFS National Grassland Units and select your study site(s).Choose AT LEAST 2! a. Study areas chosen: i. Comanche National Grassland ii. Pawnee National Grassland

2. Fit a model: For each grassland: a. Download model variables as raster layers covering your study area envelope, including: i. Two soil variables from the POLARIS dataset A. pH soil pH in H2O) B. theta_s (saturated soil water content, m3/m3) ii. Elevation from the SRTM (available from the earthaccess API) A. slope iii. Two climate scenarios from the MACAv2 dataset, accessible from Climate Toolbox. A. historical (2000-2004) B. rcp85 (worst case scenario)

   b. Calculate at least one derived topographic** variable** (slope or aspect) to use in your model. Use the xarray-spatial library, which is available in the latest earth-analytics-python environment (but will need to be installed/updated if you are working on your own machine). Note that calculated slope may not be correct if you are using a CRS with units of degrees; you should re-project into a projected coordinate system with units of meters, such as the appropriate UTM Zone.

   c. Harmonize your data - make sure that the grids for each of your layers match up. Check out the ds.rio.reproject_match() method from rioxarray.

   d. Build a model. You can use any model you wish, so long as you explain your choice. However, if you are not sure what to do, build a fuzzy logic model (see below). i. Research species of choice, and find out what optimal values are for each variable you are using (e.g. soil pH, slope, and current climatological annual precipitation). ii. For each digital number in each raster, assign a value from 0 to 1 for how close that grid square is to the optimum range (1=optimal, 0=incompatible). iii. Combine your layers by multiplying them together. This will give you a single suitability number for each square. Check out this article about raster math for more info. iii. Optionally, you may apply a threshold to make the most suitable areas pop on your map.

3. Present results in at least one figure for each grassland/climate scenario combination.

Code

1. Use PEP 8 Style Standards
2. Functions have numpy-style docstrings
3. Make functions and/or loops DRY and modular
4. Well document the code with comments