Integrating plant physiology into simulation of fire behavior and effects.

Wildfires are a global crisis, but current fire models fail to capture vegetation response to changing climate. With drought and elevated temperature increasing the importance of vegetation dynamics to fire behavior, and the advent of next generation models capable of capturing increasingly complex physical processes, we provide a renewed focus on representation of woody vegetation in fire models. Currently, the most advanced representations of fire behavior and biophysical fire effects are found in distinct classes of fine-scale models and do not capture variation in live fuel (i.e. living plant) properties. We demonstrate that plant water and carbon dynamics, which influence combustion and heat transfer into the plant and often dictate plant survival, provide the mechanistic linkage between fire behavior and effects. Our conceptual framework linking remotely sensed estimates of plant water and carbon to fine-scale models of fire behavior and effects could be a critical first step toward improving the fidelity of the coarse scale models that are now relied upon for global fire forecasting. This process-based approach will be essential to capturing the influence of physiological responses to drought and warming on live fuel conditions, strengthening the science needed to guide fire managers in an uncertain future.

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.

Lidar-derived structural-complexity data across four experimental forests.

Structural complexity refers to the three-dimensional arrangement and variability of both biotic and abiotic components of an ecosystem. Metrics that characterize structural complexity are often used to manage various aspects of ecosystem function, such as light transmittance, wildlife habitat, and biological diversity. Additionally, these metrics aid in evaluating resilience to disturbance events, including hurricanes, bark-beetle outbreaks, and wildfire. Recent advances in wildland fire modelling have facilitated the integration of forest structural complexity metrics into the QUIC-Fire model, enabling real-time prediction of fire spread and behaviour by simulating interactions between fire, weather, topography, and forest structure. While QUIC-Fire is designed to be highly adaptable, model performance depends on the availability and accuracy of local data inputs. Expanding the model's usability across different regions can be facilitated by the availability of more comprehensive and high-quality data. Thus, the primary goal behind the data products we developed was to establish a basis for collaborative research across various disciplines, particularly within the focal areas of the Southern Research Station, such as forestry, wildland fire, hydrology, soil science, and cultural resources at Bent Creek, Coweeta, Escambia, and Hitchiti Experimental Forests (EFs). Airborne laser scanning (ALS) was used to collect point-cloud data for each EF during the leaf-off season to minimize interference from foliage. Subsequent processing of the raw lidar data involved outlier detection and filtering, ground and non-ground classification, and the computation of a variety of metrics representing various aspects of topography and forest structure at both the pixel-level and the tree-level. Pixel-level topographic data products include: digital elevation model (DEM), slope, aspect, topographic position index (TPI), topographic roughness index (TRI), roughness, and flow direction. Forest structural-complexity metrics include canopy height, foliar height diversity (FHD), vertical distribution ratio (VDR), canopy rugosity, crown relief ratio (CRR), understory complexity index (UCI), vertical complexity index (VCI), canopy cover, mean vegetation height, and the standard deviation of vegetation height. Tree-level data products were computed from the point cloud using multiple algorithms to perform individual tree detection (ITD) and individual tree segmentation (ITS). The datasets have been harmonized and are openly accessible through the USDA Forest Service Research Data Archive.

Aqueous Photolysis of Water-Soluble Brown Carbon from Simulated Prescribed and Wildfire Biomass Burning.

This work, as part of the Georgia Wildland fire Simulation Experiment (G-WISE) campaign, explores the aqueous photolysis of water-soluble brown carbon (W-BrC) in biomass burning aerosols from the combustion of fuel beds collected from three distinct ecoregions in Georgia: Piedmont, Coastal Plain, and Blue Ridge. Burns were conducted under conditions representative of wildfires, which are common unplanned occurrences in Southeastern forests (low fuel moisture content), and prescribed fires, which are commonly used in forest management (higher fuel moisture content). Upon exposure to radiation from UV lamps equivalent to approximately 5 h in the atmosphere, the absorption spectra of all six samples exhibited up to 40% photobleaching in the UV range (280-400 nm) and as much as 30% photo-enhancement in the visible range (400-500 nm). Together, these two effects reduced the absorption Ångström exponent (AAE), a measure of the wavelength dependence of the spectrum, from 6.0-7.9 before photolysis to 5.0-5.7 after. Electrospray ionization ultrahigh-resolution mass spectrometry analysis shows the potential formation of oligomeric chromophores due to aqueous photolysis. This work provides insight into the impacts that aqueous photolysis has on W-BrC in biomass burning aerosols and its dependence on fuel bed composition and moisture content.

Carbon dynamics in the future forest: the importance of long-term successional legacy and climate-fire interactions.

Understanding how climate change may influence forest carbon (C) budgets requires knowledge of forest growth relationships with regional climate, long-term forest succession, and past and future disturbances, such as wildfires and timber harvesting events. We used a landscape-scale model of forest succession, wildfire, and C dynamics (LANDIS-II) to evaluate the effects of a changing climate (A2 and B1 IPCC emissions; Geophysical Fluid Dynamics Laboratory General Circulation Models) on total forest C, tree species composition, and wildfire dynamics in the Lake Tahoe Basin, California, and Nevada. The independent effects of temperature and precipitation were assessed within and among climate models. Results highlight the importance of modeling forest succession and stand development processes at the landscape scale for understanding the C cycle. Due primarily to landscape legacy effects of historic logging of the Comstock Era in the late 1880s, C sequestration may continue throughout the current century, and the forest will remain a C sink (Net Ecosystem Carbon Balance > 0), regardless of climate regime. Climate change caused increases in temperatures limited simulated C sequestration potential because of augmented fire activity and reduced establishment ability of subalpine and upper montane trees. Higher temperatures influenced forest response more than reduced precipitation. As the forest reached its potential steady state, the forest could become C neutral or a C source, and climate change could accelerate this transition. The future of forest ecosystem C cycling in many forested systems worldwide may depend more on major disturbances and landscape legacies related to land use than on projected climate change alone.

A simplified and affordable approach to forest monitoring using single terrestrial laser scans and transect sampling.

Traditional forestry, ecology, and fuels monitoring methods can be costly and error-prone, and are often used beyond their original assumptions due to difficulty or unavailability of more appropriate methods. These traditional methods tend to be rigid and may not be useful for detecting new ecological changes or required data at modern levels of precision [1]. The integration of Terrestrial Laser Scanning (TLS) methods into forest monitoring strategies can cost effectively standardize data collection, improve efficiency, and reduce error, with datasets that can easily be analyzed to better inform management decisions. Affordable (sub-$20K) off-the-shelf TLS units-such as the Leica BLK360- have been used commercially in the built environment but have untapped potential in the natural world for monitoring. Here, we provide a methodology that successfully integrates LiDAR scanning with existing monitoring methods. This new method:•Allows for simplified and quick extraction of forestry, fuels and ecological vegetation variables from a single TLS point cloud and quick transect sampling.•Streamlines the data collection process, removes sampling bias, and produces data that can be easily processed to provide inputs for models and decision support frameworks.•Is adaptable to integrate additional or new environmental measurements.

Simulating Groundcover Community Assembly in a Frequently Burned Ecosystem Using a Simple Neutral Model.

Fire is a keystone process that drives patterns of biodiversity globally. In frequently burned fire-dependent ecosystems, surface fire regimes allow for the coexistence of high plant diversity at fine scales even where soils are uniform. The mechanisms on how fire impacts groundcover community dynamics are, however, poorly understood. Because fire can act as a stochastic agent of mortality, we hypothesized that a neutral mechanism might be responsible for maintaining plant diversity. We used the demographic parameters of the unified neutral theory of biodiversity (UNTB) as a foundation to model groundcover species richness, using a southeastern US pine woodland as an example. We followed the fate of over 7,000 individuals of 123 plant species for 4 years and two prescribed burns in frequently burned Pinus palustris sites in northwest FL, USA. Using these empirical data and UNTB-based assumptions, we developed two parsimonious autonomous agent models, which were distinct by spatially explicit and implicit local recruitment processes. Using a parameter sensitivity test, we examined how empirical estimates, input species frequency distributions, and community size affected output species richness. We found that dispersal limitation was the most influential parameter, followed by mortality and birth, and that these parameters varied based on scale of the frequency distributions. Overall, these nominal parameters were useful for simulating fine-scale groundcover communities, although further empirical analysis of richness patterns, particularly related to fine-scale burn severity, is needed. This modeling framework can be utilized to examine our premise that localized groundcover assemblages are neutral communities at high fire frequencies, as well as to examine the extent to which niche-based dynamics determine community dynamics when fire frequency is altered.

A novel approach to fuel biomass sampling for 3D fuel characterization.

Surface fuels are the critical link between structure and function in frequently burned pine ecosystems, which are found globally (Williamson and Black, 1981; Rebertus et al., 1989; Glitzenstein et al., 1995) [[1], [2], [3]]. We bring fuels to the forefront of fire ecology through the concept of the Ecology of Fuels (Hiers et al. 2009) [4]. This concept describes a cyclic process between fuels, fire behavior, and fire effects, which ultimately affect future fuel distribution (Mitchell et al. 2009) [5]. Low-intensity surface fires are driven by the variability in fine-scale (sub-m level) fuels (Loudermilk et al. 2012) [6]. Traditional fuel measurement approaches do not capture this variability because they are over-generalized, and do not consider the fine-scale architecture of interwoven fuel types. Here, we introduce a new approach, the "3D fuels sampling protocol" that measures fuel biomass at the scale and dimensions useful for characterizing heterogeneous fuels found in low-intensity surface fire regimes. •Traditional fuel measurements are oversimplified, prone to sampling bias, and unrealistic for relating to fire behavior (Van Wagner, 1968; Hardy et al., 2008) [7,8].•We developed a novel field sampling approach to measuring 3D fuels using an adjustable rectangular prism sampling frame. This voxel sampling protocol records fuel biomass, occupied volume, and fuel types at multiple scales.•This method is scalable and versatile across ecosystems, and reduces sampling bias by eliminating the need for ocular estimations.