Quantitative Bioscience at the University of Tennessee

Ecological Niche Modeling

Ecological niche modeling uses computer algorithms to predict the distribution of a species across geographic space and time using environmental data. The environmental data are most often climate data (e.g. temperature, precipitation), but can include other variables such as soil type, water depth, and land cover. Ecological niche models are used in several research areas in conservation biology, ecology and evolution. 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 the set of 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.

	Department	Research Interests
Orou Gaoue Email	Ecology & Evolutionary Biology	Conservation biology, demography, plant-human interactions
Xingli Giam Email	Ecology & Evolutionary Biology	Conservation ecology, global environmental change
Louis Gross Email	Ecology & Evolutionary Biology; Mathematics	Mathematical ecology. Director, NIMBioS; Director, The Institute for Environmental Modeling (TIEM)
Yetta Jager Email	Ecology & Evolutionary Biology; Environmental Sciences Division, Oak Ridge National Lab	Quantitative ecology, hydropower, conservation biology
Monica Papeş Email	Ecology & Evolutionary Biology; Director, Spatial Analysis Lab	Ecological niche modeling, conservation science, GIS and remote sensing
Kimberly Sheldon Email	Ecology & Evolutionary Biology	Biogeography, physiological ecology, tropical ecology
Daniel Simberloff Email	Ecology & Evolutionary Biology	Biological invasions, ecology, conservation biology, biogeography

NIMBioS
1122 Volunteer Blvd., Suite 106
University of Tennessee
Knoxville, TN 37996-3410
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FAX: (865) 974-9461
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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.