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.
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