Regional data refine local abundance models: modeling plant species abundance distributions on the Central Plains
Date
2010
Authors
Young, Nicholas E., author
Stohlgren, Thomas J., advisor
Kelly, Eugene Francis, committee member
Graham, James J., committee member
Evangelista, Paul Harrison, committee member
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Abstract
Species distribution models are frequently used to predict species occurrences in novel conditions, yet few studies have examined the effects of extrapolating locally collected data to regional scale landscapes. Using boosted regression trees, I examined the issues of spatial scale and errors associated with extrapolating species distribution models developed using locally collected abundance data to regional extents for a native and alien plant species across a portion of the central plains in Colorado. Topographic, remotely sensed, land cover and soil taxonomic predictor variables were used to develop the models. Predicted means and ranges were compared among models and predictions were compared to observed values between local and regional extent models. All models had significant predictive ability (p < 0.001). My results suggested: (1) extrapolating local models to regional extents may restrict predictions; (2) modeling species abundance may prove more useful than models of species presence; (3) multiple sources of predictors may improve model results at different extents; and (4) regional data can help refine and improve local model predictions. Regional sampling designed in concert with large sampling frameworks such as the National Ecological Observatory Network, Inc (NEON) may improve our ability to monitor changes in local species abundance.
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Subject
abundance
extrapolation
central plains
boosted regression trees
Plant ecology -- Colorado
Phytogeography -- Colorado
Plants -- Habitat -- Research -- Methods
Spatial ecology -- Colorado