• fire intensity;
  • fire physics;
  • fire trap;
  • grassland;
  • juniper;
  • positive feedback;
  • regime shifts;
  • resilience;
  • restoration ecology;
  • state and transition model


  1. Restoration priorities are typically established without quantitative information on how to overcome the thresholds that preclude successful restoration of desirable ecosystem properties and services. We seek to demonstrate that quantifying ecological thresholds and incorporating them into management-oriented frameworks provide a more comprehensive perspective on how the threshold concept can be applied to achieve restoration goals.
  2. As an example, restoration actions have been largely unsuccessful when based on prevailing ecological knowledge of fire-based thresholds in nonresprouting Juniperus woodland. We build on previous threshold-based research and link well-established models from applied fire physics with a widely applied ecological positive feedback model of woody plant encroachment to introduce a more comprehensive understanding of the mechanism influencing fire intensity and juniper mortality.
  3. Our coupling of physical and ecological fire models revealed a critical knowledge gap, a lack of a quantitative estimate on the critical surface fire intensity required to cause mortality of Juniperus ashei trees, which limits the linking of scientific knowledge from these two disciplines.
  4. To quantify the relationship between fire intensity and J. ashei mortality, we input data from a previous experiment into Byram's fireline intensity model. This critical surface fire intensity–mortality threshold was estimated to be Is > 160 kJ m−1 s−1. This value establishes a specific threshold that managers should target when attempting to use restoration to collapse J. ashei woodlands.
  5. Synthesis and applications. For scientific information associated with the threshold concept to be useful to practitioners, specific information is needed that demonstrates how to use restoration activities to overcome thresholds and collapse the current, degraded state in favour of a more desired ecological state. With this in mind, we present a broadly applicable decision support model within a state and transition framework that identifies the ecological states where the surface fire intensity–mortality threshold is most likely to meet restoration objectives and provides examples of how fuel properties that drive fire intensity should be targeted in restoration to surpass this threshold.