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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: December 3-5, 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 set of conditions that allow a species to exist in a geographic area is a representation of the species ecological niche. 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 are to teach participants the concepts of ecological niche modeling, introduce them to select analytical techniques (formatting GIS data; running maximum entropy – MaxEnt – models) and present how to interpret and apply spatial analyses. Participants will be familiarized with several commonly-used and newly-available online spatial data resources. Instructors will provide datasets to use in hands-on simulations, but participants may also bring their own data if desired. Applications of ecological niche modeling covered in this tutorial are: biogeography, conservation biology, disease ecology, macroecology, and invasion biology.

This tutorial is a repeat of the NIMBioS tutorial offered May 16-18, 2018.

Intended audience. This tutorial is intended for advanced graduate students, postdocs, and faculty interested in learning how to incorporate ecological niche modeling into their research. In order to qualify for this tutorial, participants must have basic knowledge of GIS (ArcGIS, QGIS or R packages). Little to no programming is involved, with ecological niche modeling and spatial analysis conducted using existing applications (MaxEnt) and packages in ArcGIS and R.


  • Mona Papeş, Ecology & Evolutionary Biology and Spatial Analysis Lab at NIMBioS, Univ. of Tennessee, Knoxville
  • Town Peterson, Ecology & Evolutionary Biology and Biodiversity Institute, Univ. of Kansas, Lawrence, KS
  • Xiao Feng, Institute of the Environment, Univ. of Arizona, Tucson, AZ
  • Luis Escobar, Fish and Wildlife Conservation, Virginia Tech

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The application process is now closed.

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Applications of Spatial Data WordPress site.   NIMBioS has created a WordPress site to facilitate group communication and information sharing for the tutorial. This is an interactive tool for sharing resources and comments before, during and after the meeting. All participants will receive an official email from WordPress inviting you to join the site. You will be asked to click on the link in the email from WordPress to accept the invitation. Before the meeting, we encourage you to introduce yourself to the rest of the group by writing a post with some details about your background and what you hope to gain from the meeting. Full details on how to post, comment and upload files to the WordPress site are available at the site (

Live-stream icon. Live Stream. The Tutorial will be streamed live. Note that NIMBioS tutorials involve open discussion and not necessarily a succession of talks. In addition, the schedule as posted may change during the Tutorial. To view the live stream, visit

A goal of NIMBioS is to enhance the cadre of researchers capable of interdisciplinary efforts across mathematics and biology. As part of this goal, NIMBioS is committed to promoting diversity in all its activities. Diversity is considered in all its aspects, social and scientific, including gender, ethnicity, scientific field, career stage, geography and type of home institution. Questions regarding diversity issues should be directed to You can read more about our Diversity Plan on our NIMBioS Policies web page. The NIMBioS building is fully handicapped accessible.

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NSF logo. NIMBioS is 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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