How do predictor variables influence species distribution models?

Project Description : 

Invasive plants have the capacity to alter native ecosystem function and decrease biodiversity. Understanding how environmental factors (e.g., temperature and precipitation) shape the distribution of invasive plants is a critical step toward identifying regions at risk from invasion. We use ArcGIS and other ecological modeling software to project the distribution of plants under current and future climate conditions. We are seeking a student to aid in compiling physiological and location data for plant species in the western US and in building distribution models for selected plant species. The student will gain experience in using ArcGIS to modify and analyze climate and species occurrence data, MaxEnt (SDM software), and compiling and managing large spatial datasets.

More about the project: Species distribution modeling (SDM) is used to determine the climate conditions suitable for a species. A model of suitable climate can then be projected into geographic space to determine the spatial extents of potential invasion. Despite the prevalence of SDMs, it is unclear how the use of different climate data will affect models of invasion risk. Most studies rely on average temperature and precipitation and do not consider the physiological limits of the species. For this project, we will determine how the use of different climate variables impacts SDMs of plant species in the western U.S. The results of this research will be used to improve current SDM methods and will allow for more accurate predictions of invasion risk.

Student Ranks Applicable: 

Hours Per Week: 


Name of Lab: 

The Bradley Lab

Contact Person: 

Caroline Curtis

Contact E-mail:

Lab website: