No abstract

Abstract. In fire-prone forests, self-reinforcing fire behavior may generate a mosaic of vegetation types and structures. In forests long subject to fire exclusion, such feedbacks may result in forest loss when surface and canopy fuel accumulations lead to unusually severe fires. We examined drivers of fire severity in one large (>1000 km 2 ) wildfire in the western United States, the Rim Fire in the Sierra Nevada, California, and how it was influenced by severity of 21 previous fires to examine the influences on (1) the severity of the first fire since 1984 and (2) reburn severity. The random forest machine-learning statistical model was used to predict satellite-derived fire severity classes from geospatial datasets of fire history, topographic setting, weather, and vegetation type. Topography and inferred weather were the most important variables influencing the previous burn. Previous fire severity was the most important factor influencing reburn severity, and areas tended to reburn at the same severity class as the previous burn. However, areas reburned in <15 yr burned at lower severity than expected. Previous fire severity and Rim Fire severity were higher on ridges, at intermediate elevations (~750-1250 m), and on slopes <30°, indicating a consistent effect of topography on fire severity patterns in these forests. Areas burned with low severity prescribed fires burned at low severity again in the Rim Fire, and areas with long fire-free periods burned at higher severity. This fire history effect suggests that prescribed burning was an effective management tool, leading to lower fire severity in the previous burns and the subsequent reburn. Our results show that self-reinforcing fire behavior results mainly from effects of vegetation structure and fuels on fire severity and that this behavior is mediated by topographic setting and the time since last fire.

Increasing fire severity and warmer, drier postfire conditions are making forests in the western United States (West) vulnerable to ecological transformation. Yet, the relative importance of and interactions between these drivers of forest change remain unresolved, particularly over upcoming decades. Here, we assess how the interactive impacts of changing climate and wildfire activity influenced conifer regeneration after 334 wildfires, using a dataset of postfire conifer regeneration from 10,230 field plots. Our findings highlight declining regeneration capacity across the West over the past four decades for the eight dominant conifer species studied. Postfire regeneration is sensitive to high-severity fire, which limits seed availability, and postfire climate, which influences seedling establishment. In the near-term, projected differences in recruitment probability between low- and high-severity fire scenarios were larger than projected climate change impacts for most species, suggesting that reductions in fire severity, and resultant impacts on seed availability, could partially offset expected climate-driven declines in postfire regeneration. Across 40 to 42% of the study area, we project postfire conifer regeneration to be likely following low-severity but not high-severity fire under future climate scenarios (2031 to 2050). However, increasingly warm, dry climate conditions are projected to eventually outweigh the influence of fire severity and seed availability. The percent of the study area considered unlikely to experience conifer regeneration, regardless of fire severity, increased from 5% in 1981 to 2000 to 26 to 31% by mid-century, highlighting a limited time window over which management actions that reduce fire severity may effectively support postfire conifer regeneration.

Fire severity patterns are driven by interactions between fire, vegetation, and terrain, and they generate legacy effects that influence future fire severity. A century of fire exclusion and fuel buildup has eroded legacy effects, and contemporary fire severity patterns may diverge from historical patterns. In recent decades, area burned and area burned at high severity have increased and landscapes are transitioning back to an active fire regime where disturbance legacies will again play a strong role in determining fire severity. Understanding the drivers of fire severity is crucial for anticipating future fire severity patterns as active fire regimes are reestablished. We identified drivers of fire severity in the Klamath Mountains, a landscape with an active fire regime, using two machine learning statistical models: one model for nonreburns (n = 92) and one model for reburns (n = 61). Both models predicted low better than moderate or high-severity fire. Fire severity drivers contrasted sharply between non-reburns and reburns. Fire weather and fuels were dominant controls in non-reburns, while previous burn severity, fuel characteristics, and time since last fire were drivers for reburns. In reburns, areas initially burned at low (high) severity burned the same way again. This tendency was sufficiently strong that reburn fire severity could be predicted equally well with only severity of the previous fire in the model. Thus, reburn fire severity is more predictable than severity in non-reburns that are driven by the stochastic influences of fire weather. Reburn severity in aggregate was also higher than non-reburn severity suggesting a positive feedback effect that could contribute to an upward drift in fire severity as area burned increases. Terrain had low importance in both models. This indicates strong terrain controls in the past may not carry into the future. Low-and moderate-severity fire effects were prevalent in non-reburns under moderate fire weather and selfreinforcing behavior maintained these effects in reburns even under more extreme weather, particularly in reburns within 10 yr. Our findings suggest deliberate use of wildfire and prescribed fire under moderate conditions would increase fire resilience in landscapes transitioning to an active fire regime.