Accurate and reliable predictions of invasive species distributions are urgently needed by land managers for developing management plans and monitoring new potential areas of establishment. Presence-only species distribution models are commonly used in these evaluations, however they are rarely tested with independent data over time or compared with presence-absence models fit with the same presence data. Using Maxent, we developed a presence-only model of invasive cheatgrass (Bromus tectorum L.) distribution in Rocky Mountain National Park, Colorado, USA in 2007 fit with limited data, and then tested the model with independent presence and absence data collected between 2008 and 2013. This model was verified using threshold dependent and threshold independent evaluation metrics. Next, we developed a Maxent model with cheatgrass presence data from 2007 through 2013 (i.e. Maxent 2013), and compared this model to a presence-absence method (i.e., generalized linear model; GLM 2013) using the same data. Threshold dependent and threshold independent evaluation metrics suggested Maxent 2013 outperformed GLM 2013, and a two-tailed Wilcoxon signed rank test indicated relative probability outputs were not significantly different between the models in geographic space. Based on known presences and absences of cheatgrass collected in the field, the Maxent 2013 and GLM 2013 relative probability outputs were highly correlated at absence locations but less correlated at presence locations. A Kappa comparison of Maxent 2007 and Maxent 2013 binary output provides evidence that Maxent is robust when fit with limited data. Our results indicate Maxent is an appropriate model for use when land management objectives are supported by limited resources and thus require a conservative, but highly accurate estimate of habitat suitability for invasive species on the landscape.
['Amanda M. West', 'Sunil Kumar', 'Cynthia S. Brown', 'Thomas J. Stohlgren', 'Jim Bromberg']
Field validation of an invasive species Maxent model