This species distribution model was produced for a limited extent within the DRECP region, defined as a union of 10 km buffers of the Colorado, Mojave, and Owens Rivers and detections, at 270 m resolution with 54 detections points obtained Feb. 2013 from CNDDB (California Department of Fish and Wildlife, Biogeographic Data Branch); USFWS Carlsbad Fish & Wildlife Office (http://www.fws.gov/carlsbad/GIS/CFWOGIS.html); eBird (http://ebird.org/content/ebird), and ORNIS (http://www.ornisnet.org/).
The model was built with the following 4 environmental predictors (provided to CBI by Frank Davisâ Biogeography Lab at UC Santa Barbara, created for the CA Energy Commissionâs project âCumulative Biological Impacts Framework for Solar Energy in the CA Desertâ, 500-10-021) in order of importance:
WHR habitat rating (focal mean (25 m grid) of arithmetic mean of WHR ratings for cover, feeding, and reproduction calculated for area approximating the minimum habitat patch, nesting home range, or activity area for the species based on DRECP species biology notes and other sources (for this species, a circle with 225 m radius). The resulting grid was re-aggregated to 270m based on the median of cell scores in the block;
Perennial water features, as indicated by the USGS NHD feature codes 39004, 39009, 39010, 39011, 39012, 45800, 46006, and 46602.Â Categorical presence/absence, indicating the presence of any perennial water feature within each 270m pixel;
Integrated solar radiation (WH/m2, ESRI Spatial Analyst Area Solar Radiation).Â Derived from the interior of 30m NED DEM tiles buffered to 300m.Â Integrated from 2012-02-29 to 2012-05-30.Â Average integrated value in each 270m pixel;
Soil thickness, produced by A. &. L. Flint.
This model has a 10-fold cross validated AUC score of 0.950 (standard deviation 0.023). Results are preliminary and have not yet been reviewed by expert biologists.Â
Both continuous probability surfaces and binary layers are available. The binary layer depicting predicted suitable habitat was derived using the maximum training sensitivity and specificity threshold (0.117).