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Quantitative Bioscience at the University of Tennessee

Ecological Niche Modeling

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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.

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

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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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