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oai:mountainscholar.org:10217/459822025-12-30T03:13:32Zcom_10217_100532com_10217_100000com_10217_100430com_10217_100303col_10217_100538col_10217_100433

Young, Nicholas E., author Stohlgren, Thomas J., advisor Kelly, Eugene Francis, committee member Graham, James J., committee member Evangelista, Paul Harrison, committee member Colorado 2007-01-03T04:55:13Z 2007-01-03T04:55:13Z 2010 Young_colostate_0053N_10228.pdf ETDF2010100009FRWS http://hdl.handle.net/10217/45982 https://doi.org/10.25675/3.018323 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. born digital masters theses English eng Colorado State University. Libraries 2000-2019 Copyright and other restrictions may apply. User is responsible for compliance with all applicable laws. For information about copyright law, please see https://libguides.colostate.edu/copyright. abundance extrapolation central plains boosted regression trees Plant ecology -- Colorado Phytogeography -- Colorado Plants -- Habitat -- Research -- Methods Spatial ecology -- Colorado Regional data refine local abundance models: modeling plant species abundance distributions on the Central Plains Text Master of Science (M.S.) Masters Forest, Rangeland, and Watershed Stewardship Colorado State University This Item is protected by copyright and/or related rights (https://rightsstatements.org/vocab/InC/1.0/). You are free to use this Item in any way that is permitted by the copyright and related rights legislation that applies to your use. For other uses you need to obtain permission from the rights-holder(s). open.access