​Beverly and McLoughlin (2019) showed that real fires in Alberta did not occur preferentially in simulated burn probability hotspots mapped with a popular landscape fire assessment tool (Burn-P3). You can read more about the details here. This work disrupted a well-entrenched assumption within the landscape fire modeling community in Canada that burn probability simulation is an effective method for identifying which landscape locations are most likely to burn in the years that follow.

In exploring the underlying reasons why burn probability simulations did not align well with real world fires, we were challenged to think differently about the problems inherent in landscape fire assessment. This led us to pursue a fundamentally different process-oriented approach to the study of landscape pattern in fire-prone ecosystems and resulted in a novel new metric of landscape fire. The landscape fire exposure metric (Beverly, McLoughlin and Chapman 2021) is a numeric rating of the potential for fire transmission to a location given surrounding fuel composition and configuration, irrespective of weather or other fire controls. Exposure to hazardous fuels can be assessed in minutes for large landscapes by one variable at virtually no marginal cost using rudimentary GIS techniques. This simple, deterministic, univariate metric of fire exposure aligned well with real-world fires observed in our study area and represents a departure from computationally complex and data-intensive approaches for characterizing fire spread potential across landscapes.​

​A note about simplicity in science

Our simple metric speaks to a fundamental concept in the philosophy of science. An accessible online backgrounder on simplicity in science can be found here.  The role of simplicity is summed up nicely as follows: "There are many ways in which simplicity might be regarded as a desirable feature of scientific theories. Simpler theories are frequently said to be more “beautiful” or more “elegant” than their rivals; they might also be easier to understand and to work with. However, according to many scientists and philosophers, simplicity is not something that is merely to be hoped for in theories; nor is it something that we should only strive for after we have already selected a theory that we believe to be on the right track (for example, by trying to find a simpler formulation of an accepted theory). Rather, the claim is that simplicity should actually be one of the key criteria that we use to evaluate which of a set of rival theories is, in fact, the best theory, given the available evidence: other things being equal, the simplest theory consistent with the data is the best one."