Fuel treatment planning: fragmenting high fuel load areas while maintaining availability and connectivity of faunal habitat

TL;DR

This paper introduces a Mixed Integer Programming model for strategic fuel treatment planning that balances wildfire risk reduction with ecological habitat connectivity, ensuring the protection of fauna and their habitats over time.

Contribution

It presents a novel optimization model that simultaneously considers fuel hazard reduction and ecological habitat connectivity, a balance not previously addressed in such detail.

Findings

The model effectively reduces wildfire risk while maintaining habitat connectivity.

Habitat connectivity constraints outperform neighborhood constraints in conservation effectiveness.

The approach is validated through computational experiments on hypothetical landscapes.

Abstract

Reducing the fuel load in fire-prone landscapes is aimed at mitigating the risk of catastrophic wildfires but there are ecological consequences. Maintaining habitat for fauna of both sufficient extent and connectivity while fragmenting areas of high fuel loads presents land managers with seemingly contrasting objectives. Faced with this dichotomy, we propose a Mixed Integer Programming (MIP) model that can optimally schedule fuel treatments to reduce fuel hazards by fragmenting high fuel load regions while considering critical ecological requirements over time and space. The model takes into account both the frequency of fire that vegetation can tolerate and the frequency of fire necessary for fire-dependent species. Our approach also ensures that suitable alternate habitat is available and accessible to fauna affected by a treated area. More importantly, to conserve fauna the model sets a minimum acceptable target for the connectivity of habitat at any time . These factors are all included in the formulation of a model that yields a multi-period spatially-explicit schedule for treatment planning. Our approach is then demonstrated in a series of computational experiments with hypothetical landscapes, a single vegetation type and a group of faunal species with the same habitat requirements. Our experiments show that it is possible to reduce the risk of wildfires while ensuring sufficient connectivity of habitat over both space and time. Furthermore, it is demonstrated that the habitat connectivity constraint is more effective than neighbourhood habitat constraints. This is critical for the conservation of fauna and of special concern for vulnerable or endangered species.