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

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

Perspectives on Disconnects Between Scientific Information and Management Decisions on Post-fire Recovery in Western US

Environmental regulations frequently mandate the use of "best available" science, but ensuring that it is used in decisions around the use and protection of natural resources is often challenging. In the Western US, this relationship between science and management is at the forefront of post-fire land management decisions. Recent fires, post-fire threats (e.g. flooding, erosion), and the role of fire in ecosystem health combine to make post-fire management highly visible and often controversial. This paper uses post-fire management to present a framework for understanding why disconnects between science and management decisions may occur. We argue that attributes of agencies, such as their political or financial incentives, can limit how effectively science is incorporated into decision-making. At the other end of the spectrum, the lack of synthesis or limited data in science can result in disconnects between science-based analysis of post-fire effects and agency policy and decisions. Disconnects also occur because of the interaction between the attributes of agencies and the attributes of science, such as their different spatial and temporal scales of interest. After offering examples of these disconnects in post-fire treatment, the paper concludes with recommendations to reduce disconnects by improving monitoring, increasing synthesis of scientific findings, and directing social-science research toward identifying and deepening understanding of these disconnects

A simulation analysis of the impact of forest fire suppression policies on rekindles

Rekindles have a significant presence in the Portuguese forest fire management system and an important impact on fire suppression resources in particular (Pacheco, Claro, and Oliveira 2014). In most of the cases, mop-up is made exclusively with water, without suitable tools to carry out the work effectively, allowing the fire to rekindle. Thus, it's pretended to develop a discrete event simulation (DES) of rekindles as a rework problem and, after this, assay the impact of different suppression policies on rekindles

Development of a danger index of forest fires by using multinomial logistic regression and its application in different zones of the region of Castile and Leon (Spain)

La aplicación de las nuevas tecnologías ha mejorado los sistemas de protección contra incendios forestales. Como resultado, es sencillo poder predecir el comportamiento de futuros incendios teniendo en cuenta algunos factores tales como: modelos de combustible, velocidad de viento, humedad relativa,... Un nuevo punto de vista para la determinación del riesgo de incendio forestal es analizado en este estudio mediante la combinación de dos métodos diferentes que ha sido llevada a cabo para desarrollar un nuevo índice de peligro: la aplicación de un programa informático de Sistemas de Información Geográfica (SIG) y un modelo estadístico que está basado en una regresión logística multinomial. Este nuevo índice de peligro se centra en las diferentes zonas dentro de un mismo término municipal en vez de en sus límites administrativos.Producción Vegetal y Recursos forestalesMáster en Ingeniería de Monte