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This document introduces a simulation-optimization approach to design strategic fuelbreaks that protect the fire-prone forest landscapes of Alberta, Canada. By conceptualizing the forest as a fuel network graph, the authors use spatial fire growth models to map fireplains, which represent the potential directions and likelihoods of wildfire transmission between different locations. The study compares various mathematical strategies, specifically the Critical Node Detection Problem and the Critical Edge Removal Problem, to determine the most effective way to compartmentalize the landscape into isolated segments. Unlike traditional methods that only look at local hazard levels, these network-based models successfully prevent major fire spread corridors and disrupt connectivity along prevailing wind paths. This document provides a robust framework for managers to prioritize wildfire risk mitigation under limited budgets.

This document presents a network optimization approach designed to strategically place forest fuel treatments, such as prescribed burns, to curtail the spread of wildfires. By representing a landscape as a network of connected patches, the authors use a modified Critical Node Detection model to identify and remove key nodes to effectively fragment fire pathways. Their methodology incorporates directional fire spread probabilities derived from stochastic simulations, allowing the model to account for real-world factors like wind patterns and topography rather than relying solely on local fuel loads. The study further refines the planning process by introducing constraints on the spatial contiguity and timing of treatments, ensuring that recommended fuel breaks are logistically feasible and safe to implement. The researchers demonstrate through a case study in Kootenay National Park that this optimized partitioning is significantly more effective at reducing landscape connectivity than traditional strategies that focus only on individual high-risk sites.