These data are statistical model outputs for Lane Mountain
milk-vetch (
Astragalus jaegerianus
)
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 USDA ecoregion
subsections with occurrences and 10km buffer of occurrences, at 270 m
resolution with 211 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 ten 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:
Precipitation of warmest quarter
(mm);
Soil water content at wilting
point, produced by A. & L. Flint;
Minimum temperature of coldest period
(°C, x10);
Annual precipitation (mm);
Soil porosity, produced by A. and
L. Flint;
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;
Topographic relief in the 270m cell
estimated as the standard deviation of elevations from
30m digital elevation model;
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.
This model has a 10-fold cross validated AUC score of 0.976
(standard deviation 0.006). The binary layer depicting predicted suitable habitat was
derived using the maximum training sensitivity and specificity threshold
(0.251).