Wildfires Threaten Saguaro Cacti in Pima County, AZ

In the summer of 2020, the Bighorn Fire burned nearly 120,000 acres of desert near my home in Tucson, Arizona. Not only do wildfires threaten the lives of residents, they are also a growing threat to our native flora and fauna.

The Saguaro Cactus is the largest species of cactus and endemic to the United States South West and Baja California. It can grow up to 78 feet tall and provides food and shelter for other desert dwelling species.

Global Warming is increasing the intensity and frequency of wildfires all across the world. Mitigating the climate crisis requires a two part solution: countries must make drastic changes to cut their carbon emissions, while local governments prepare solutions unique to their location.

The purpose of my project is to locate areas for concentrated fire prevention based on their threat to Saguaro cacti.

To identify where Saguaros are at risk from fire, I decided to create a probability model of areas where wildfires are likely to occur based on environmental factors.

To model of areas of fire risk, I ran a multivariable logistic regression to produce a binary model. A multivariable regression determines the amount of influence various factors have on a certain outcome.

The first step of my process was to compare the locations of known wildfires to a control group. I generated random points throughout Pima county and extracted the values of land cover, relief, precipitation and temperature at each point into a table. Then, I performed the same operation to the wildfire events, averaging their variable values for each fire.

On the right is a portion of the regression data table. The value 1 in the column "Case Field" identifies the object as a event group (wildfire) location while 0 represents a point from the control group. The regression will compare the fire locations to the control group to isolate each variable's impact on the occurence of a fire.

I ran the logistic regression in R, a programming language used for data manipulation.

The rightmost column of the output displays the significance, or p value, of each variable in predicting the locations of wildfires. The statistical standard for p-value is < 0.05. Based on this rule, total relief is not statistically significant because its p-value = 0.556.

After removing total relief, I ran the regression again and the p-values were all < 0.05. The first column of the regression labelled "estimate" can now be used to construct the probability model.

A limitation in my project was the resolution of the temperature and precipitation data I used. This caused my resulting probability model to have a coarse resolution, which reduces its accuracy in predicting wildfire events.

I compared Saguaro point locations to the areas of wildfire risk predicted by the binary model. The result displays only the Saguaros at risk from wildfires.

Then, I created a heat map based on the greatest concentration of at-risk Saguaros. The heat map is divided into 20 categories, where higher values represent a greater number of Saguaros. I selected the locations with values of 18 or greater, which yielded the four locations indicated on the map below.

Based on my project, the four locations displayed above should be the subject of concentrated fire prevention efforts in order to protect Saguaro cacti. This would require cooperation with tribal authorities and the National Parks Service.

The number of wildfires are increasing across Pima County, and targeted solutions are the best way to mitigate the worst effects of the climate crisis.