Species distribution models are frequently used to predict species occurrences in novel conditions, yet few studies have examined the consequences of extrapolating locally collected data to regional landscapes. Using boosted regression trees, we examined errors associated with extrapolating models developed with locally collected abundance data to regional-scale spatial extents for a native and non-native plant species across the northeastern central plains of Colorado. We developed models to predict the spatial distribution of plant species abundance using topographic, remotely sensed, land cover and soil taxonomic predictor variables. We evaluated predicted mean and range abundance values between local and regional models and compared these predictions to observed values.
Results/Conclusions
All models had significant predictive capability (p < 0.001). We show that: (1) extrapolating local models to regional extents may restrict predictions, (2) regional data can help refine and improve local predictions, and (3) boosted regression trees can be useful to model and predict plant species abundance. Regional sampling designed in concert with large sampling frameworks such as the National Ecological Observatory Network may improve our ability to monitor changes in local species abundance.