Tehachapi slender salamander - Species Distribution Model, DRECP

Apr 26, 2013 (Last modified Dec 3, 2013)
Description:
These data are statistical model outputs for Tehachapi slender salamander (Batrachoseps stebbinsi ) species distribution, completed by CBI. Predictions of habitat occupancy were generated from Maxent models for the DRECP.

This species distribution model was produced for a limited extent within the DRECP region, defined as a union of 10 km buffers of the Tehachapi slender salamander’s CWHR range (California Department of Fish and Wildlife, Biogeographic Data Branch, 2008) and detections, at 270 m resolution with 33 detections points obtained Feb. 2013 from CNDDB (California Department of Fish and Wildlife, Biogeographic Data Branch) and Herpnet (http://www.herpnet2.org/).The model was built with the following 5 environmental predictors (provided to CBI by Frank Davis’ Biogeography Lab at UC Santa Barbara, created for the CA Energy Commission’s project “Cumulative Biological Impacts Framework for Solar Energy in the CA Desert”, 500-10-021) in order of importance:

Integrated solar radiation (WH/m2, ESRI Spatial Analyst Area Solar Radiation).  Derived from the interior of 30m NED DEM tiles buffered to 300m.  Integrated from 2012-02-29 to 2012-05-30.  Average integrated value in each 270m pixel;Flow accumulation (ESRI Spatial Analyst Flow Accumulation), calculated from 90m HydroSHEDS flow direction rasters.  90m model data were log(x+1) transformed.  Maximum of the transformed values in each 270m pixel;Minimum temperature of coldest period (°C, x10);Topographic relief in the 270m cell estimated as the standard deviation of elevations from 30m digital elevation model;Soil available water storage (cm) from 0-50cm, derived from SSURGO or STATSGO where SSURGO was unavailable.  The mapunit-area-weighted average of aws050wta in table muaggatt.This model has a 10-fold cross validated AUC score of 0.920 (standard deviation 0.075). 

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 (0.094).
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Conservation Biology Institute
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