TxDOT Research Library - TxDOT Research Project

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Predictive Mapping of Potentially Listed Rare Plant Species

What Was the Need?

The U. S. Fish and Wildlife Service (USFWS) has identified a number of rare plants that may be proposed for listing as threatened or endangered under the federal Endangered Species Act (ESA) in the next several years. Because the distribution and habitat requirements for many of these species is poorly known, TxDOT could be subject to increased numbers of surveys for these plants in the near future. Without well informed tools to identify areas that potentially support these species, TxDOT could be subject to significant survey cost and delays to ensure project compliance with the ESA.

What's the Solution?

This project will identify existing populations and define habitat requirements (e.g., soils, land-use, and community associations) for these potentially-listed species. Distribution maps will be created and researchers will develop predictive metrics and models that will enable TxDOT to understand the likelihood of impacting these species in future projects, including identification of suitable habitat as well as areas that are clearly unsuitable habitat for the target species.

Project Number

0-6973

Status

Completed

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Project Details

Start Date

6/6/2018

End Date

10/31/2021

Performing Institution(s)

Tarleton State University

Research Team

RS: Hemanta Kafley

Sponsor

Project Manager

Martin Dassi

Contract Specialist

Tami Graham

TxDOT Advisor

Andrew Blair, AUS
Becki Perkins, SJT
John Moravec, BRY
Mark Fisher, ENV
Matt Buckingham, LFK

Amount Funded

$312,801

Notes

Texas Parks and Wildlife list of rare plant species is available at https://tpwd.texas.gov/huntwild/wild/wildlife_diversity/nongame/tcap/sgcn.phtml
Texas Administrative Code Title 31 TAC Chapter 69.8 lists current threatened and endangered https://texreg.sos.state.tx.us/public/readtac$ext.TacPage?sl=R&app=9&p_dir=&p_rloc=&p_tloc=&p_ploc=&pg=1&p_tac=&ti=31&pt=2&ch=69&rl=8

Source

TxDOT RFP 18-278 Developing Predictive Metrics for Rare Plant Occurrences