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NIMBioS Tutorial: Applications of Spatial Data

Ecological Niche Modeling

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Topic: Applications of Spatial Data: Ecological Niche Modeling

Meeting dates: May 16-18, 2018

Location: NIMBioS at the University of Tennessee, Knoxville

Mona Papeş, Ecology & Evolutionary Biology and Spatial Analysis Lab at NIMBioS, Univ. of Tennessee, Knoxville
Greg Wiggins, NIMBioS Education & Outreach Coordinator

Objectives: The distribution of a species may be influenced by an array of factors. The combination of these factors results in the ecological niche, the set of conditions that allow a species to exist in a geographic area. However, defining these conditions is difficult, due to the complexity of natural systems. One approach to characterizing the ecological niche uses spatial data, GIS software, and modeling algorithms. The objectives of this tutorial were to teach participants the concepts of ecological niche modeling, introduce them to select analytical techniques (formatting data in GIS; running Maximum Entropy (MaxEnt) models), and present how to interpret and apply spatial analyses. Participants were familiarized with several commonly-used and/or newly-available online spatial data resources. Participants were provided datasets to use in hands-on simulations, but could also bring their own data if desired.

Intended audience. This tutorial was intended for advanced graduate students, postdocs, and faculty interested in learning how to incorporate ecological niche modeling into their research. Some basic knowledge of GIS software and ecology preferred. Little to no programming was involved, with ecological niche modeling and spatial analysis conducted using existing applications (MaxEnt) and packages in QGIS and R.


  • Mona Papeş
  • Town Peterson, Ecology & Evolutionary Biology and Biodiversity Institute, Univ. of Kansas, Lawrence, KS
  • Xiao Feng, Institute of the Environment, Univ. of Arizona, Tucson, AZ


Summary Report. TBA

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Applications of Spatial Data WordPress site  

Playlist of presentation videos

Evaluation Report

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From 2008 until early 2021, NIMBioS was supported by the National Science Foundation through NSF Award #DBI-1300426, with additional support from The University of Tennessee, Knoxville. Any opinions, findings, and conclusions or recommendations expressed in this material are those of the author(s) and do not necessarily reflect the views of the National Science Foundation.
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