Investing in rangeland restoration in the Arid West, USA: countering the effects of an invasive weed on the long-term fire cycle

J Environ Manage. 2009 Nov-Dec;91(2):370-9. doi: 10.1016/j.jenvman.2009.09.004. Epub 2009 Sep 25.

Abstract

In large areas of the arid western United States, much of which are federally managed, fire frequencies and associated management costs are escalating as flammable, invasive cheatgrass (Bromus tectorum) increases its stronghold. Cheatgrass invasion and the subsequent increase in fire frequency result in the loss of native vegetation, less predictable forage availability for livestock and wildlife, and increased costs and risk associated with firefighting. Revegetation following fire on land that is partially invaded by cheatgrass can reduce both the dominance of cheatgrass and its associated high fire rate. Thus restoration can be viewed as an investment in fire-prevention and, if native seed is used, an investment in maintaining native vegetation on the landscape. Here we develop and employ a Markov model of vegetation dynamics for the sagebrush steppe ecosystem to predict vegetation change and management costs under different intensities and types of post-fire revegetation. We use the results to estimate the minimum total cost curves for maintaining native vegetation on the landscape and for preventing cheatgrass dominance. Our results show that across a variety of model parameter possibilities, increased investment in post-fire revegetation reduces long-term fire management costs by more than enough to offset the costs of revegetation. These results support that a policy of intensive post-fire revegetation will reduce long-term management costs for this ecosystem, in addition to providing environmental benefits. This information may help justify costs associated with revegetation and raise the priority of restoration in federal land budgets.

Publication types

  • Research Support, Non-U.S. Gov't

MeSH terms

  • Conservation of Natural Resources / economics*
  • Costs and Cost Analysis
  • Ecosystem*
  • Fires*
  • Markov Chains
  • Models, Theoretical
  • United States