Predictive accuracy of post-fire conifer death declines over time in models based on crown and bole injury.

A key uncertainty of empirical models of post-fire tree mortality is understanding the drivers of elevated post-fire mortality several years following fire, known as delayed mortality. Delayed mortality can represent a substantial fraction of mortality, particularly for large trees that are a conservation focus in western US coniferous forests. Current post-fire tree mortality models have undergone limited evaluation of how injury level and time since fire interact to influence model accuracy and predictor variable importance. Less severe injuries potentially serve as an indicator for vulnerability to additional stressors such as bark beetle attack or moisture stress. We used a collection of 164,293 individual tree records to examine post-fire tree mortality in eight western USA conifers: Abies concolor, Abies grandis, Calocedrus decurrens, Larix occidentalis, Pinus contorta, Pinus lambertiana, Pinus ponderosa, and Pseudotsuga menziesii. We evaluated the importance of fire injury predictors on discriminating between surviving trees versus immediate and delayed post-fire mortality. We fit balanced random forest models for each species using cumulative tree mortality from 1 to 5-years post-fire. We compared these results to multi-class random forest models using first-year mortality, 2-5-year mortality, and survival 5-years post-fire as a response variable. Crown volume scorched, diameter at breast height, and relative bark char height, were used as predictor variables. The cumulative mortality models all predicted trees that died within 1-year of fire with high accuracy but failed to predict 2-5-year mortality. The multi-class models were an improvement but had lower accuracy for predicting 2-5-year mortality. Multi-class model accuracies ranged from 85% to 95% across all species for predicting 1-year post-fire mortality, 42%-71% for predicting 2-5-year mortality, and 64%-85% for predicting trees that lived past 5-years. Our study highlights the differences in tree species tolerance to fire injury and suggests that including second-order predictors such as beetle attack or climatic water stress before and after fire will be critical to improve accuracy and better understand the mechanisms and patterns of fire-caused tree death. Random forest models have potential for management applications such as post-fire harvesting and simulating future stand dynamics.

Understanding flammability and bark thickness in the genus Pinus using a phylogenetic approach.

Pinus species dominate fire-prone ecosystems throughout the northern hemisphere. Their litter drive fires that control plant community flammability and multiple ecological processes. To better understand the patterns and mechanisms of pine flammability, we measured leaf characteristics (needle length and thickness) and conducted combustion experiments on litter from 31 species. We paired flammability results with bark accumulation data and used phylogenetic generalized least squares regression to examine relationships between physical traits and flammability. Pine flammability varied widely among pines: flame heights and fuel consumption varied three-fold, and flaming and smoldering durations varied three- to six-fold. Subgenus Pinus species were the most flammable and subgenus Strobus species had the lowest flammability. Needle length was the best predictor of flammability with a significant interaction with subgenus, suggesting that flammability of pines in subgenus Strobus was more affected by physical traits than pines in subgenus Pinus. Species in the subgenus Pinus that accumulated outer bark rapidly also had high flammability, while the relationship was not significant in subgenus Strobus. These results highlight the diverse patterns of flammability in North American pines and the complexity in the mechanisms causing differential flammability.

Tree crown injury from wildland fires: causes, measurement and ecological and physiological consequences.

The dead foliage of scorched crowns is one of the most conspicuous signatures of wildland fires. Globally, crown scorch from fires in savannas, woodlands and forests causes tree stress and death across diverse taxa. The term crown scorch, however, is inconsistently and ambiguously defined in the literature, causing confusion and conflicting interpretation of results. Furthermore, the underlying mechanisms causing foliage death from fire are poorly understood. The consequences of crown scorch - alterations in physiological, biogeochemical and ecological processes and ecosystem recovery pathways - remain largely unexamined. Most research on the topic assumes the mechanism of leaf and bud death is exposure to lethal air temperatures, with few direct measurements of lethal heating thresholds. Notable information gaps include how energy transfer injures and kills leaves and buds, how nutrients, carbohydrates, and hormones respond, and what physiological consequences lead to mortality. We clarify definitions to encourage use of unified terminology for foliage and bud necrosis resulting from fire. We review the current understanding of the physical mechanisms driving foliar injury, discuss the physiological responses, and explore novel ecological consequences of crown injury from fire. From these elements, we propose research needs for the increasingly interdisciplinary study of fire effects.

Allometry of the pyrophytic Aristida in fire-maintained longleaf pine-wiregrass ecosystems.

PREMISE OF THE STUDY: Aboveground biomass (AGB) of herbaceous vegetation is a primary source of fuel in frequent surface fires that maintain grasslands, savannas, and woodlands. Methods for nondestructively estimating AGB are required to understand the mechanisms by which fuels affect fire behavior and the effects of time since the last burn. We developed allometric equations to estimate AGB in wiregrass (Aristida beyrichiana/A. stricta), a dominant bunchgrass in Pinus palustris ecosystems and a key species for ecological restoration. METHODS: We collected wiregrass from North Carolina to Florida, across a range of time-since-last burn and site types. We tested 32 mixed effect models to see which predictors were best at predicting live, dead, and total AGB. We also examined how time since burn (TSB) affected the live-to-dead ratio (LDR) using regression. KEY RESULTS: Wiregrass AGB was found to increase with increasing latitude (relative to tussock volume), possibly due to an increase in precipitation, and was greater on more fertile clay soils and flatwoods than on sandy soils. The LDR decreased as a power function with TSB, resulting in rapid accumulation of dead, highly flammable, biomass in the fire-free period. CONCLUSIONS: Greater biomass will support fires of higher intensity. Our models can be useful in the parameterization of future physics-based models to predict fire behavior. Understanding the environmental variables that influence the allometry of wiregrass should help increase the precision of AGB estimates and the subsequent effects on fire behavior and effects on neighboring vegetation.