Topographic information improves simulated patterns of post-fire conifer regeneration in the southwest United States.

The western United States is projected to experience more frequent and severe wildfires in the future due to drier and hotter climate conditions, exacerbating destructive wildfire impacts on forest ecosystems such as tree mortality and unsuccessful post-fire regeneration. While empirical studies have revealed strong relationships between topographical information and plant regeneration, ecological processes in ecosystem models have either not fully addressed topography-mediated effects on the probability of plant regeneration, or the probability is only controlled by climate-related factors, for example, water and light stresses. In this study, we incorporated seedling survival data based on a planting experiment in the footprint of the 2011 Las Conchas Fire into the Photosynthesis and EvapoTranspiration (PnET) extension of the LANDIS-II model by adding topographic and an additional climatic variable to the probability of regeneration. The modified algorithm included topographic parameters such as heat load index and ground slope and spring precipitation. We ran simulations on the Las Conchas Fire landscape for 2012-2099 using observed and projected climate data (i.e., Representative Concentration Pathway 4.5 and 8.5). Our modification significantly reduced the number of regeneration events of three common southwestern conifer tree species (piñon, ponderosa pine, and Douglas-fir), leading to decreases in aboveground biomass, regardless of climate scenario. The modified algorithm decreased regeneration at higher elevations and increased regeneration at lower elevations relative to the original algorithm. Regenerations of three species also decreased in eastern aspects. Our findings suggest that ecosystem models may overestimate post-fire regeneration events in the southwest United States. To better represent regeneration processes following wildfire, ecosystem models need refinement to better account for the range of factors that influence tree seedling establishment. This will improve model utility for projecting the combined effects of climate and wildfire on tree species distributions.

Prioritizing forest fuels treatments based on the probability of high-severity fire restores adaptive capacity in Sierran forests.

In frequent fire forests of the western United States, a legacy of fire suppression coupled with increases in fire weather severity have altered fire regimes and vegetation dynamics. When coupled with projected climate change, these conditions have the potential to lead to vegetation type change and altered carbon (C) dynamics. In the Sierra Nevada, fuels reduction approaches that include mechanical thinning followed by regular prescribed fire are one approach to restore the ability of the ecosystem to tolerate episodic fire and still sequester C. Yet, the spatial extent of the area requiring treatment makes widespread treatment implementation unlikely. We sought to determine if a priori knowledge of where uncharacteristic wildfire is most probable could be used to optimize the placement of fuels treatments in a Sierra Nevada watershed. We developed two treatment placement strategies: the naive strategy, based on treating all operationally available area and the optimized strategy, which only treated areas where crown-killing fires were most probable. We ran forecast simulations using projected climate data through 2,100 to determine how the treatments differed in terms of C sequestration, fire severity, and C emissions relative to a no-management scenario. We found that in both the short (20 years) and long (100 years) term, both management scenarios increased C stability, reduced burn severity, and consequently emitted less C as a result of wildfires than no-management. Across all metrics, both scenarios performed the same, but the optimized treatment required significantly less C removal (naive=0.42 Tg C, optimized=0.25 Tg C) to achieve the same treatment efficacy. Given the extent of western forests in need of fire restoration, efficiently allocating treatments is a critical task if we are going to restore adaptive capacity in frequent-fire forests.