These data are statistical model outputs for Parish's
phacelia (Phacelia parishii) 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 10km buffer of
occurrences, USDA ecoregion subsections with occurrences, and 5 adjacent
subsections, at 270 m resolution with 19 detections points obtained March 2013
from CNDDB
(California
Department of Fish and Wildlife, Biogeographic Data Branch) and
Consortium of California Herbaria (http://ucjeps.berkeley.edu/consortium/).
The model was built with the following 9 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:
Topographic relief in the 270m cell
estimated as the standard deviation of elevations from 30m digital elevation
model;
Precipitation of warmest quarter
(mm);
Aridity index (annual precipitation
(mm)/ potential evapotranspiration (mm/annual), x100);
Minimum temperature of coldest period
(°C, x10);
Soil thickness, produced by A.
&. L. Flint;
Soil pH (pH scale) from 0-50cm,
derived from SSURGO or STATSGO where SSURGO was unavailable. The mapunit area weighted average of the soil
component percent area weighted average of the soil component horizon depth
weighted average of ph1to1h2o_r in table chorizon;
Playas, as the union of USGS NHD
feature code 36100 and those features delineated by VegCAMP and GAP. Categorical presence/absence;
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;
Temperature seasonality (C of V,
x100).
This model has a 10-fold cross validated AUC score of 0.912
(standard deviation 0.094). The binary layer depicting predicted suitable habitat was
derived using the maximum training sensitivity and specificity threshold (0.165).