Tehachapi pocket mouse - Species Distribution Model, DRECP

This dataset is statistical model output for Tehachapi pocket mouse (Perognathus alticolus inexpectatus) 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 a 15 km buffer of the white-eared pocket mouse’s (Perognathus alticolus) CWHR range (California Department of Fish and Wildlife, Biogeographic Data Branch, 2008) and Tehachapi pocket mouse detections, at 270 m resolution with 39 detections points obtained Feb. 2013 from CNDDB (California Department of Fish and Wildlife, Biogeographic Data Branch), MaNIS (http://manisnet.org/), and species locality data compiled by the California Mammal Species of Special Concern (MSSC) project (http://databasin.org/groups/88e0ca8e-62db-4e52-8332-8e4ab781346c).

The model was built with the following 7 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:

Maximum temperature of warmest period (°C, x10);

Soil thickness, produced by A. &. L. Flint;

Temperature seasonality (C of V, x100);

Percent grassland (averaged over a circular moving window with a radius of 150 m);

Percent desert scrub (averaged over a circular moving window with a radius of 150 m);

Percent chaparral (averaged over a circular moving window with a radius of 150 m); and

Soil field capacity (Mpa), produced by A. & L. Flint, derived from SSURGO or STATSGO where SSURGO was unavailable.

This model has a 10-fold cross validated AUC score of 0.899 (standard deviation 0.049).

Areas with land use/cover types of agriculture (deciduous orchard/vineyard, irrigated field), urban, or lacustrine were masked out (converted to ‘nodata’).

The binary layer depicting predicted suitable habitat was derived using the maximum training sensitivity and specificity threshold (0.214).