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Species Distribution and Climate Envelope Models

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Species distribution models link locations where an animal is known to occur with environmental layers. Once the relationship, or niche, is described, the model can be applied to locations that have not been sampled. Many types of environmental layers are available in a Geographic Information System. Remotely sensed vegetation data, topographic data, climate data, and road density are a few examples of environmental factors that can constrain where an animal will occur. Climate envelope models only use climate data to describe the niche.  In Alaska, climate layers developed from current or historic climate station data are available from the Scenarios Network for Alaska and Arctic Planning (SNAP; www.snap.uaf.edu). SNAP also forecasts future climate conditions by downscaling Global Climate Models (GCMs). Climate envelope models are used to forecast how a species distribution may shift under changing climate conditions.

Kenai National Wildlife Refuge staff model bird, arthropod, and plant distributions using data collected as part of our Long-Term Ecological Monitoring program (LTEMP).  We use a variety of techniques, including the computer learning algorithm called Random ForestsTM, to build our models.  Species distribution models from the 2004 and 2006 LTEMP data will be used as a monitoring metric to document distributional shifts (Magness and Morton 2008). Climate envelope models are also used to explore how species distribution may shift under changing climate conditions.  We are also modeling how vegetation types may change under alternative, future climate scenarios over the next 100 years. Our vegetation map was developed by classifying remotely sensed data (LandSAT).

Magness, D.R., F. Huettmann, & J.M. Morton. 2008. Using Random Forests to provide predicted species distribution maps as a metric for ecological inventory & monitoring programs. Pages 209-229 in T.G. Smolinski, M.G. Milanova & A-E. Hassanien (eds.).  Applications of Computational Intelligence in Biology: Current Trends and Open Problems. Studies in Computational Intelligence, Vol. 122, Springer-Verlag Berlin Heidelberg. 428pp.

Last Updated: Dec 19, 2012
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