Cheatgrass (Bromus tectorum) Species Distribution Model, DRECP

May 11, 2016 (Last modified Oct 6, 2016)
Description:
This species distribution dataset depicts the occurrence probability for Bromus tectorum (cheatgrass),which is one of four of the exotic annual grasses of most concern when it comes to fire in the desert.  These species can provide persistent, continuous fuel beds for fires to spread, enhancing the potential for increased fire size and frequency (Brooks and Minnich 2006).


The species distribution models were developed using MaxEnt (Phillips and Dudik 2008, Elith et al. 2011), a map-based modeling software that has performed well with presence-only data (Elith et al. 2006). The model assigns a probability of species’ occurrence in each grid cell based on a machine-learning algorithm that identifies the species’ best distribution pattern through iterative contrasts between occurrence locations and a sample of 10 000 background points.


We downloaded the occurrence data from the Global Biodiversity Information Facility (GBIF) (www.gbif.org), and for each species, we ran three cross-validated model runs using ten thousand random background points.  This resulted in a total of 95 presence records for training and 48 for testing for B. tectorum. We used hinge features, linear, and quadratic, with an increase in regularization with a beta of 2.5, to reduce model overfitting (Elith et al. 2011). We also used jackknife tests to estimate predictor variable importance.  We used predictor variables hypothesized to be associated with plant distribution patterns in desert ecosystems.


The average test AUC for the replicate runs was 0.898 for B. tectorum.
Data Provided By:
Alexandra D. Syphard, Ph. D. 
Senior Research Scientist
Conservation Biology Institute
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Creative Commons LicenseThis work is licensed under a Creative Commons Attribution 3.0 License.
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