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

• Published in: Applied Mathematical Modelling Vol. 54; pp. 298 - 310
• Main Authors: Rachmawati, Ramya, Ozlen, Melih, Hearne, John, Reinke, Karin
• Format: Journal Article
• Language: English
• Published: New York Elsevier Inc 01.02.2018; Elsevier BV
• Subjects: Endangered species; Environmental modelling; Forest & brush fires; Fragmentation; Fuels; Habitat conservation; Habitats; Hazards; Integer programming; Landscape; Landscape management; Linear programming; Mixed integer; Optimisation; Risk assessment; Vegetation; Wildfire; Wildfires; Wildlife conservation; Wildfire; Landscape management; Environmental modelling; Habitat conservation; Optimisation
• ISSN: 0307-904X; 1088-8691; 0307-904X
• DOI: 10.1016/j.apm.2017.09.045

•Extends medium-term fuel treatment scheduling to include wildlife considerations.•Reduces fuel load while simultaneously maintains habitat availability.•Maintains both extent of habitat and connectivity of habitat.•Shows that landscape connectivity constraints are more effective than neighbourhood constraints. 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 fragment areas of high fuel loads 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.