Integrating flora, fauna, and indigenous practices into spatial optimization for prescribed burning

Area of Science:

• Ecology
• Environmental Management
• Conservation Biology

Background:

• Climate change intensifies wildfire activity globally.
• Existing fire management strategies require adaptation to promote ecological coexistence with fire.
• A framework for integrating fire coexistence factors into regional planning is currently lacking.

Purpose of the Study:

• To develop and apply a novel approach for spatially optimizing prescribed burning by integrating fire risk and coexistence capacity.
• To identify priority areas for prescribed burning in fire-prone mountainous regions.
• To provide guidance for sustainable fire hazard management.

Main Methods:

• Utilized machine learning and deep learning to predict fire risk and identify areas with high coexistence potential.
• Applied the Zonation 5 software for spatial prioritization of management areas.
• Integrated ecological adaptations of flora and fauna, and traditional land management practices as coexistence factors.

Main Results:

• A significant spatial correlation was found between fire risk and coexistence capacity in the Jialing River watershed.
• High-value clusters of fire risk and coexistence capacity were concentrated in the central and southern regions.
• 5% of fire-prone forests with high coexistence capacity were identified as priority areas for prescribed burning.

Conclusions:

• The integrated approach effectively identifies priority areas for prescribed burning by balancing fire risk and coexistence capacity.
• This methodology offers valuable guidance for policymakers and land planners in fire-prone mountainous regions.
• Embracing fire coexistence is crucial for enhancing long-term ecosystem resilience and sustainable fire management.