Climatic and Landscape Influences on Fire Regimes from 1984 to 2010 in the Western United States

Integrating the effects of driving forces on ecosystem services into ecological management: A case study from Sichuan Province, China

Driving forces are the factors that lead to the observed changes in the quantity and quality of ecosystem services (ESs). The relationship between driving forces and ESs involves considerable scale-related information. Place-based ecological management requires this information to support local sustainable development. Despite the importance of scale in ES research, most studies have only examined the association between ESs and their drivers at a single level, and few studies have examined this relationship at various scales or analyzed spatial heterogeneity. The purpose of this paper is to explore the significance of the scale-dependent effects of drivers on ESs for localized ecological management. The biophysical values of ESs were calculated using several ecological simulation models. The effects of driving forces on ESs were explored using the geographically weighted regression (GWR) model. Variations in the effects of driving forces on ESs were examined at three scales: provincial, ecoregional, and subecoregional scales. Finally, canonical correlation analysis was used to identify the major environmental factors associated with these variations in each ecoregion. Our results show that (1) the distribution of soil conservation and water yield is highly heterogeneous; (2) four driving forces have significant positive and negative impacts on soil conservation and water yield, and their effects on the two services vary spatially (p < 0.05); (3) the impacts of drivers on ESs vary across different spatial scales, with a corresponding shift in the related environmental factors; and (4) in the study area, at the provincial scale, physical, topographical, and biophysical factors were key factors associated with the variations in the relationship between ESs and drivers, and at the ecoregional and subecoregional scales, physical, socioeconomic, topographical, and biophysical factors all contributed to these changes. Our results suggest that significant differences in topographical conditions (e.g., altitude, slope) can be incorporated for exploring the relationship between drivers and ESs and optimizing ecological management at the provincial scale, whereas significant differences in physical and socioeconomic conditions (e.g., urbanization levels, human activity, vegetation coverage) are more meaningful for localized ecological management at the ecoregional and subecological scales. These findings provide a basis for understanding the relationship between drivers and ESs at multiple scales as well as guidelines for improving localized ecological management and achieving sustainable development.

Analysis of Forest Fire Dynamics, Distribution and Main Drivers in the Atlantic Forest

The fire susceptibility of the Atlantic Forest has largely increased over the past two decades due to a combination of climate change and anthropogenic factors such as land cover change and human modification. High rates of forest fragmentation have contributed to escalating fires in this imperilled global biodiversity hotspot. Understanding fire patterns is essential to developing an effective forest fire management strategy. In this research, we utilized the Random Forest (RF) machine learning approach for identifying the role of climatic and anthropogenic factors in influencing fire occurrence probability and mapping the spatial distribution of fire risk. We found that the Normalized Difference Vegetation Index value and climate variables (i.e., temperature and solar radiation) were significant drivers of fire occurrence risk. Results also confirm that forest fragmentation increases with fire density in the region.

Identifying Key Drivers of Wildfires in the Contiguous US Using Machine Learning and Game Theory Interpretation

Understanding the complex interrelationships between wildfire and its environmental and anthropogenic controls is crucial for wildfire modeling and management. Although machine learning (ML) models have yielded significant improvements in wildfire predictions, their limited interpretability has been an obstacle for their use in advancing understanding of wildfires. This study builds an ML model incorporating predictors of local meteorology, land-surface characteristics, and socioeconomic variables to predict monthly burned area at grid cells of 0.25° × 0.25° resolution over the contiguous United States. Besides these predictors, we construct and include predictors representing the large-scale circulation patterns conducive to wildfires, which largely improves the temporal correlations in several regions by 14%-44%. The Shapley additive explanation is introduced to quantify the contributions of the predictors to burned area. Results show a key role of longitude and latitude in delineating fire regimes with different temporal patterns of burned area. The model captures the physical relationship between burned area and vapor pressure deficit, relative humidity (RH), and energy release component (ERC), in agreement with the prior findings. Aggregating the contribution of predictor variables of all the grids by region, analyses show that ERC is the major contributor accounting for 14%-27% to large burned areas in the western US. In contrast, there is no leading factor contributing to large burned areas in the eastern US, although large-scale circulation patterns featuring less active upper-level ridge-trough and low RH two months earlier in winter contribute relatively more to large burned areas in spring in the southeastern US.

Classifying Large Wildfires in the United States by Land Cover

Fire is an ecological process that also has socio-economic effects. To learn more about fire occurrence, I examined relationships between land classes and about 12,000 spatially delineated large wildfires (defined here as uncontrolled fires ≥200 ha, although definitions vary) during 1999 to 2017 in the conterminous United States. Using random forests, extreme gradient boosting, and c5.0 classifiers, I modeled all fires, first years (1999 to 2002), last years (2014 to 2017), the eastern, central, and western United States and seven ecoregions. The three classifiers performed well (true positive rates 0.82 to 0.94) at modeling all fires and fires by year, region, and ecoregion. The random forests classifier did not predict to other time intervals or regions as well as other classifiers and models were not constant in time and space. For example, the eastern region overpredicted fires in the western region and models for the western region underpredicted fires in the eastern region. Overall, greater abundance of herbaceous grasslands, or herbaceous wetlands in the eastern region, and evergreen forest and low abundance of crops and pasture characterized most large fires, even with regional differences. The 14 states in the northeastern United States with no or few large fires contained limited herbaceous area and abundant crops or developed lands. Herbaceous vegetation was the most important variable for fire occurrences in the western region. Lack of crops was most important for fires in the central region and a lack of pasture, crops, and developed open space was most important for fires in the eastern region. A combination of wildlands vegetation was most influential for most ecoregions, although herbaceous vegetation alone and lack of pasture, crops, and developed open space also were influential. Despite departure from historical fire regimes, these models demonstrated that herbaceous vegetation remains necessary for fires and that evergreen forests in particular are fire-prone, while reduction of vegetation surrounding housing developments will help provide a buffer to reduce large fires.

Smoldering and flaming biomass wood smoke inhibit respiratory responses in mice

Background: Acute and chronic exposures to biomass wildfire smoke pose significant health risks to firefighters and impacted communities. Susceptible populations such as asthmatics may be particularly sensitive to wildfire effects. We examined pulmonary responses to biomass smoke generated from combustion of peat, oak, or eucalyptus in control and house dust mite (HDM)-allergic mice. Methods: Mice were exposed 1 h/d for 2 consecutive days to emissions from each fuel type under smoldering or flaming conditions (∼40 or ∼3.3 mg PM/m³, respectively) while maintaining comparable CO levels (∼60-120 ppm). Results: Control and allergic mice reduced breathing frequency during exposure to all biomass emissions compared with pre-exposure to clean air. Smoldering eucalyptus and oak, but not peat, further reduced frequency compared to flaming conditions in control and allergic groups, while also reducing minute volume and peak inspiratory flow in control mice. Several biochemical and cellular markers of lung injury and inflammation were suppressed by all biomass emission types in both HDM-allergic and control mice. Control mice exposed to flaming eucalyptus at different PM concentrations (C) and times (T) with the same C × T product had a greater decrease in breathing frequency with high concentration acute exposure compared with lower concentration episodic exposure. This decrease was ameliorated by PM HEPA filtration, indicating that the respiratory changes were partially mediated by biomass smoke particles. Conclusion: These data show that exposure to smoldering eucalyptus or oak smoke inhibits respiratory responses to a greater degree than peat smoke. Anti-inflammatory effects of CO may possibly contribute to smoke-induced suppression of allergic inflammatory responses.

Study on Climate and Grassland Fire in HulunBuir, Inner Mongolia Autonomous Region, China

Grassland fire is one of the most important disturbance factors of the natural ecosystem. Climate factors influence the occurrence and development of grassland fire. An analysis of the climate conditions of fire occurrence can form the basis for a study of the temporal and spatial variability of grassland fire. The purpose of this paper is to study the effects of monthly time scale climate factors on the occurrence of grassland fire in HulunBuir, located in the northeast of the Inner Mongolia Autonomous Region in China. Based on the logistic regression method, we used the moderate-resolution imaging spectroradiometer (MODIS) active fire data products named thermal anomalies/fire daily L3 Global 1km (MOD14A1 (Terra) and MYD14A1 (Aqua)) and associated climate data for HulunBuir from 2000 to 2010, and established the model of grassland fire climate index. The results showed that monthly maximum temperature, monthly sunshine hours and monthly average wind speed were all positively correlated with the fire climate index; monthly precipitation, monthly average temperature, monthly average relative humidity, monthly minimum relative humidity and the number of days with monthly precipitation greater than or equal to 5 mm were all negatively correlated with the fire climate index. We used the active fire data from 2011 to 2014 to validate the fire climate index during this time period, and the validation result was good (Pearson’s correlation coefficient was 0.578), which showed that the fire climate index model was suitable for analyzing the occurrence of grassland fire in HulunBuir. Analyses were conducted on the temporal and spatial distribution of the fire climate index from January to December in the years 2011–2014; it could be seen that from March to May and from September to October, the fire climate index was higher, and that the fire climate index of the other months is relatively low. The zones with higher fire climate index are mainly distributed in Xin Barag Youqi, Xin Barag Zuoqi, Zalantun Shi, Oroqen Zizhiqi, and Molidawa Zizhiqi; the zones with medium fire climate index are mainly distributed in Chen Barag Qi, Ewenkizu Zizhiqi, Manzhouli Shi, and Arun Qi; and the zones with lower fire climate index are mainly distributed in Genhe Shi, Ergun city, Yakeshi Shi, and Hailar Shi. The results of this study will contribute to the quantitative assessment and management of early warning and forecasting for mid-to long-term grassland fire risk in HulunBuir.

Factor contribution to fire occurrence, size, and burn probability in a subtropical coniferous forest in East China

The contribution of factors including fuel type, fire-weather conditions, topography and human activity to fire regime attributes (e.g. fire occurrence, size distribution and severity) has been intensively discussed. The relative importance of those factors in explaining the burn probability (BP), which is critical in terms of fire risk management, has been insufficiently addressed. Focusing on a subtropical coniferous forest with strong human disturbance in East China, our main objective was to evaluate and compare the relative importance of fuel composition, topography, and human activity for fire occurrence, size and BP. Local BP distribution was derived with stochastic fire simulation approach using detailed historical fire data (1990-2010) and forest-resource survey results, based on which our factor contribution analysis was carried out. Our results indicated that fuel composition had the greatest relative importance in explaining fire occurrence and size, but human activity explained most of the variance in BP. This implies that the influence of human activity is amplified through the process of overlapping repeated ignition and spreading events. This result emphasizes the status of strong human disturbance in local fire processes. It further confirms the need for a holistic perspective on factor contribution to fire likelihood, rather than focusing on individual fire regime attributes, for the purpose of fire risk management.