Jennifer L. Beverly

In northern forest ecosystems, fuel reduction treatments with thinning and pruning are used to decrease the potential for crown fire development. Despite increasing use of proactive fuel management in western Canada, the effects of fuel reduction treatments on fire behaviour in black spruce are not well understood and the timelines over which treatments remain effective have been largely unstudied. Post-treatment changes in stand structure can potentially result from windthrow, regeneration, and crown expansion, which can be expected to alter susceptibility to crown fire over time. Fourteen fuel inventory plots were established in natural black spruce forests in the Slave Lake region of Alberta. Seven plots were treated and immediately remeasured. Measurements were conducted in 2012 and 2013, before and after the treatment event; and all plots were remeasured roughly a decade later in 2022 and 2023. Field measurements documenting stand structure and composition were compared between treated and untreated groups of plots. Statistical analysis was used to investigate differences in regeneration density and composition; changes in crown width and percent cover; and windthrow. Regeneration density in treated plots was consistent with that observed in untreated plots; however, species composition differed markedly with tamarack regeneration dominating treated plots and black spruce dominating untreated plots. Crown widths and percent canopy cover did not change significantly in either treated or untreated plots during the roughly 10 years elapsed between measurements, indicating that crown spacing objectives of the treatment were maintained. Probability of windthrow increased with increasing distance to the nearest tree and was more likely in treated stands, which suggests clustered arrangements of trees in thinned stands could potentially reduce windthrow in fuel treated plots. Post-treatment stand structural changes following fuel reduction in black spruce forests of western Canada are complex. Increased windthrow likelihood was accompanied by beneficial shifts in species composition of regenerating trees and no evidence of crown width expansion among retained large trees.

FireexposuR (v1.2.0) is an R package that automates wildfire exposure methodologies presented in several scientific publications (Beverly et al., 2010, 2021; Beverly & Forbes, 2023), providing an accessible and adaptable platform to a broad user base. Wildfire exposure is a numeric rating of potential wildfire transmission to, or from, a location based on the composition and configuration of the surrounding wildland fuels (i.e., flammable vegetation). Exposure assessments can be applied at multiple spatial scales (e.g., neighbourhood, region, country) to inform a wide range of management objectives and decisions (e.g., fuel management priorities, evacuation planning, land-use planning, habitat conservation, firefighting resource optimization); and are particularly well-suited for scenario and contingency planning. fireexposuR has functions for computational analysis and generating visualizations of results in summary tables, plots, and maps.

Background: Stand-replacing wildfires and eastern spruce budworm outbreaks (Choristoneura fumiferana; SBW) are important disturbances in the boreal forest. SBW defoliation can affect fire behaviour by altering fuel loads and connectivity, thereby promoting the transition of low-activity surface fires into crown fires. However, little is known about how these altered fuels impact the effectiveness of fire suppression. Aims: To assess key drivers of initial attack (IA) success in Ontario’s boreal forest and determine if incorporating SBW defoliation data improves predictive models. Methods: We developed random forest models of fire containment using established predictors including fire weather, fire size at IA and region. We then evaluated if the inclusion of time since SBW defoliation improved model performance. Key results: Fire size at IA was the most influential variable for determining whether a fire escaped containment. Contrary to our hypothesis, we did not find evidence that SBW defoliation greatly improved model performance. Conclusions and implications: The size of the fire at IA was the most important variable in determining successful containment. Although budworm defoliation has been shown to affect other aspects of fire hazard, we were unable to identify an influence on IA success. Future work could benefit from focused investigation into how historical SBW defoliation affects fire behaviour.

Assessing wildfire hazard at the landscape level in Portugal with low-cost and time-saving methods is necessary to guide land and fire managers and protect communities at risk. This study applied a landscape metric developed in Canada to map wildfire exposure in Mainland Portugal between 1995 and 2018. The wildfire exposure metric was computed and validated by examining exposure within subsequently burned areas over five years: 1995, 2007, 2010, 2015, and 2018. Landscape fire exposure was computed by the proportion of neighborhood cells in a 100 m resolution grid that include hazardous fuel types. The resultant exposure metric analyzes the amount of land cover type in the area of a site that will either aid or prevent fire spread. The distribution of exposure levels remained relatively stable over time, decreasing just 0.5% from 1995 to 2018. Approximately 80% of burned areas occurred in sites with substantial exposure (i.e., ≥ 80%). This exposure metric, originally developed in Canada, aligned well with wildfires modulated by Portuguese climate and vegetation, leading to its successful validation. This study uses a simple, time-saving method to show high and low wildfire exposure areas, allowing managers to plan mitigation efforts at different scales, with potential applications for other countries facing large wildfire events.

Portugal’s increasing wildfire frequency has led to home destruction, large areas burned, ecological damage, and economic loss, emphasizing the need for effective fire exposure assessments. This study builds on a Canadian approach to wildfire exposure and evaluates wildfire exposure in the Portuguese municipality of Mafra, using artificial territories (AT) as a proxy for the wildland–urban interface (WUI) and integrates land use land cover (LULC) data with a neighborhood analysis to map exposure at the municipal scale. Fire exposure was assessed for three fire transmission distances: radiant heat (RH, <30 m), short-range spotting (SRS, <100 m), and longer-range spotting (LRS, 100–500 m) using fine resolution (5 m) LULC data. Results revealed that while AT generally exhibited lower exposure (<16% “very high” exposure), adjacent hazardous LULC subtypes significantly increase wildfire hazard, with up to 51% of LULC subtypes classified as “very high exposure”. Field validation confirmed the accuracy of exposure maps, supporting their use in wildfire risk reduction strategies. This cost-effective, scalable approach offers actionable insights for forest and land managers, civil protection agencies, and policymakers, aiding in fuel management prioritization, community preparedness, and the design of evacuation planning. The methodology is adaptable to other fire-prone regions, particularly mediterranean landscapes.

Wildfires burned an estimated 2.2 million hectares in Alberta in 2023. We describe key attributes of the fires relative to historical fires and fire seasons and offer a perspective on potentially influential factors. Thirty-six large fires ≥10 000 ha generated 95% of annual area burned. Individually, these fires exhibited sizes, fire weather, and behaviour consistent with historical fires; there were simply far more of them in 2023. Thirteen fires reported in early May were ignited by lightning and reached final sizes ≥10 000 ha, revealing a previously unrecognized threat. Historically, large lightning-ignited fires reported before mid-May occur just once per decade on average. Collectively, 18 large fires reported in early May coincided with drier conditions compared with 18 large fires reported after mid-May. Early May fire weather was also warmer and drier than historical weather. The early May fire group was a temporally concentrated outbreak in west-central Alberta and coincided with extreme potential rate of fire spread. Large fires reported after mid-May were intermittent through to September, concentrated in northern regions and coincided with extreme potential for fuel consumption. Individually, these two spatiotemporal modes of fire season severity (outbreak, intermittent) produced annual burned areas on par with historical extremes. Together, the 2023 multi-modal pattern of fire season severity amplified area burned far above anything previously recorded. Potential contributing factors include climate warming, hemispheric teleconnections, phenology and exhaustion of suppression resources. Implications for future fire seasons, research and management are discussed.

Background: Fire management agencies use the proportion of fires classified as initial attack (IA) success as a suppression performance metric, making IA success a common indicator of suppression effectiveness in research. The criteria and definition for IA success vary based on operational objectives, making comparisons across studies difficult. Aims: To examine the sensitivity of different time and size-based IA success definitions on model predictive accuracy and compare different modelling approaches. Methods: Using 30 years of historical fire report data from Ontario, Canada (n = 26,171), we developed logistic regression models, bagged classification trees and random forest models to predict IA success for eight different definitions. Model predictive accuracy, sensitivity and specificity were assessed on an independent validation dataset. Key results: The eight definitions classified between 79 and 98% of fires as IA successes. There was no clear pattern between model strength across prediction metrics and IA success definition. Logistic regression generally outperformed machine learning methods in classifying IA escapes. Conclusions and implications: The definition of IA success does not greatly impact model performance across the modelling techniques used. Models of IA success and suppression-system performance metrics should be set with specific research or operational objectives in mind.

Background: Land cover information is routinely used to represent fuel conditions in wildfire hazard, risk and exposure assessments. Readily available land cover data options that vary in resolution, extent, cost and purpose of collection have become increasingly accessible in recent years. Aim: This study investigates the sensitivity of community-scale wildfire exposure assessments to different land cover information products used to identify hazardous fuel. Methods: Ten versions of a community wildfire exposure assessment were conducted for each of five case study locations in Alberta, Canada, by varying the input land cover data. Proportional and spatial distribution of hazardous fuels and classified exposure are compared across datasets and communities. Key results: We found proportional and spatial variation of exposure values between datasets within each community, but the nature of this variation differed between communities. Land cover classification definitions and scale were important factors that led to inconsistencies in assessment results. Conclusions: Readily available land cover information products may not be suitable for exposure assessments at a localised scale without consideration of unique context and local knowledge of the assessment area. Implications: Results may inform fuel data selection considerations for improved results in various wildfire applications at localised scales.