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Wildland fires have a multitude of ecological effects in forests, woodlands, and savannas across the globe. A major focus of past research has been on tree mortality from fire, as trees provide a vast range of biological services. We assembled a database of individual-tree records from prescribed fires and wildfires in the United States. The Fire and Tree Mortality (FTM) database includes records from 164,293 individual trees with records of fire injury (crown scorch, bole char, etc.), tree diameter, and either mortality or top-kill up to ten years post-fire. Data span 142 species and 62 genera, from 409 fires occurring from 1981-2016. Additional variables such as insect attack are included when available. The FTM database can be used to evaluate individual fire-caused mortality models for pre-fire planning and post-fire decision support, to develop improved models, and to explore general patterns of individual fire-induced tree death. The database can also be used to identify knowledge gaps that could be addressed in future research., Measurement(s)plant morphology trait • tree mortality • fire • tree fire injury • wildfireTechnology Type(s)digital curationFactor Type(s)year of data collection • geographic location of fire • tree fire injurySample Characteristic - OrganismtreesSample Characteristic - Environmentforest ecosystemSample Characteristic - LocationCascades Region • Blue Mountains • Far Northern Rockies • Sierra Nevada • Piedmont Province • Region of Piedmont • Atlantic and Gulf Coastal Plain Floristic Province • Northern Rocky Mountains Provincial Park Machine-accessible metadata file describing the reported data: 10.6084/m9.figshare.12369293

Reliable estimates of pre-burn biomass and fuel consumption are important to estimate wildland fire emissions and assist in prescribed burn planning. We present empirical models for predicting fuel consumption in natural fuels from 60 prescribed fires in ponderosa pine-dominated forests in the western US and 60 prescribed fires in long-needle pine forests in the southeastern US. There was high variability across sites, but total surface fuel biomass was generally much lower on southern sites (23.0 ± 11.6 Mg ha −1 ) than western sites (61.5 ± 35.8 Mg ha −1 ). Differences in surface fuel composition, pre-burn loading and fuel consumption between the southern and western pine consumption datasets justify the development of regional models for predicting fuel consumption. Southern pine models of herb, shrub and 1-h consumption have close model fit with narrow prediction intervals across the range of sampled values. Relationships between 10-h and 100-h pre-burn loading and consumption produced models with reasonable fit but with no significant correlation with fuel moisture. Model fit of litter and duff consumption models was relatively poor compared to the other southern fuel categories. Western models were developed for 1-h, 10-h and 100-h fine wood, sound coarse wood, rotten coarse wood, litter and duff. All western models had high coefficients of variability, and model residuals indicate higher uncertainty with increasing pre-burn biomass. Although empirical models are widely used, they have limitations in that they are constrained by burning conditions and ranges of predictor variables.

Prescribed fire is widely used to manage fuels in high-frequency, low-severity fire regimes including pine flatwoods of the southeastern USA where prescribed burning during the growing season (the frost-free period during the calendar year) has become more common in recent decades. Growing season prescribed fires address ecological management objectives that focus on increasing herb cover and decreasing shrub cover. The shift from shrub to herb dominance due to burning in the growing season corresponds to a change in surface fuels that could affect fire behavior, yet little has been done to assess the potential effects. We examined the effects of season-of-burn on shrub and herbaceous fuel layers and predicted fire behavior at replicate plots on frequently burned mesic pine flatwoods for two season-of-burn treatments (growing and dormant season prescribed fires) in two geographic regions in northern Florida. The Fuel Characteristic Classification System was used to construct a representative fuelbed for each plot at each sampling time to predict fire behavior. Predicted fire behavior was tested for correlation with measured surface fuel properties to determine if there was an effect from differences in fuels characteristics across treatments. In addition, fire temperature was measured in situ as a proxy for fire intensity and tested for treatment effects on the re-growth of live surface fuels. Compared to single dormant season burns, our single growing season burns caused no changes to live understory fuels and had no detectable effect on fire behavior, although predicted rate of spread and flame length were significantly reduced after all prescribed burns. Shrub cover and predicted fire behavior were, however, significantly different between geographic regions, and shrub height was significantly affected by fire temperature. Predicted fire behavior was strongly correlated with measures of the litter and herb strata. Results from this study suggest that land managers should not initially expect large changes in understory fuel properties or potential fire behavior from a shift to burning during the growing season and show that geographic location and fire intensity had significant effects on live fuels and potential fire behavior.

Reliable predictions of fuel consumption are critical in the eastern United States (US), where prescribed burning is frequently applied to forests and air quality is of increasing concern. CONSUME and the First Order Fire Effects Model (FOFEM), predictive models developed to estimate fuel consumption and emissions from wildland fires, have not been systematically evaluated for application in the eastern US using the same validation data set. In this study, we compiled a fuel consumption data set from 54 operational prescribed fires (43 pine and 11 mixed hardwood sites) to assess each model’s uncertainties and application limits. Regions of indifference between measured and predicted values by fuel category and forest type represent the potential error that modelers could incur in estimating fuel consumption by category. Overall, FOFEM predictions have narrower regions of indifference than CONSUME and suggest better correspondence between measured and predicted consumption. However, both models offer reliable predictions of live fuel (shrubs and herbaceous vegetation) and 1 h fine fuels. Results suggest that CONSUME and FOFEM can be improved in their predictive capability for woody fuel, litter, and duff consumption for eastern US forests. Because of their high biomass and potential smoke management problems, refining estimates of litter and duff consumption is of particular importance.

The Orchidaceae family is one of the most diverse plant families on earth, accounting for 10% of total plant biodiversity. However, many species of orchids are threatened with extinction from habit...

Wildfire is a major forest disturbance in interior Alaska that can both directly and indirectly alter ecological processes. We used a combination of pre- and post-fire forest floor depths and post-fire ground cover assessments measured in the field, and high-resolution airborne hyperspectral imagery, to map forest floor conditions after the 2004 Taylor Complex in Alaska’s boreal forest. We applied a linear spectral unmixing model with five endmembers representing green moss, non-photosynthetic moss, charred moss, ash and soil to reflectance data to produce fractional cover maps. Our study sites spanned low to moderately high burn severity, and both black and white spruce forest types; high cover of green or non-photosynthetic moss in the remotely sensed imagery indicated low consumption, whereas high cover of charred moss, ash or soil indicated higher consumption. Strong relationships (R2 = 0.5 to 0.6) between green moss estimated from the imagery and both post-fire depth and percentage consumption suggest that potential burn severity may be predicted by a map of green (live) moss. Given that the depth of the post-fire forest floor is ecologically significant, the method of mapping the condition of the organic forest floor with hyperspectral imagery presented here may be a useful tool to assess the effect of future fires in the boreal region.