  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.
