PS 47-100 - Inferred presence of threatened and endangered species along road projects

Wednesday, August 8, 2007
Exhibit Halls 1 and 2, San Jose McEnery Convention Center
Jonathan Mates-Muchin and Christopher States, Environmental Sciences and Permits, California Department of Transportation, Oakland, CA
The requirements of federal agencies to comply with the Endangered Species Act often strain state and local resources.  Some requirements include a minimum of two-year presence/absence studies of threatened and endangered species and conclusive evidence supporting protection plans prior to consultation.  State and local agencies often limit front-end cost by inferring presence of species that are proposed to be in an area based on visual surveys and/or by declaration of critical habitat.  Inferring presence shift the financial burden to mitigation and compensation.  Because statistical literature emphasizes wildlife management, there are few studies that assess presence/absence of species on road projects.  A literature review of studies utilizing inferring presence from existing biological assessment from the California Department of Transportation (CALTRANS) was conducted and a model was created emphasizing studies that did not obtain access to all parcels.   With this information, I created a model to assess habitat types and estimate, conservatively, the probability of threatened or endangered species in adjacent inaccessible parcel.   I hypothesized based on completed surveys from fragmented locations that species will not be present on adjacent parcels where the are no differences in several factors: habitat type, presence of water, soil type, tree canopy cover.  Each of these resources is given a value within a model that predicts the likelihood of presence or absence of environmental resources.  Once the model is created it will be tested against other CALTRANS projects. I observed that habitat type is a conservative predictor for presence or absence on endangered species on inaccessible parcels.  Utilizing these statistical models, state and local agencies will improve the accuracy by which they can make decisions and improve the success by which the state or local agency can consult with federal and state regulatory agencies.
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