Fire Drivers Research Articles

Study on the socioeconomic and climatic effects of forest fire incidence in the Changbai Mountain area based on a cross-classified multilevel model

The occurrence of forest fires is determined by fuel, climate and ignition sources at different temporal and spatial scales. However, most analyses are performed only on a single scale, and few comprehensive statistical analyses elaborate on the cross-scale interaction of fire drivers. Understanding the differential effects of socioeconomic and climatic factors on forest fire incidence across different administrative and climatic scales can provide new information for wildfire studies. In this study, we applied a cross-classified multilevel model with forest fire incidence data derived from Moderate Resolution Imaging Spectroradiometer products (MODIS 14A1) to explore the relationship between the spatial distribution of fire incidence and socioeconomic and climatic factors at different levels and to estimate the effects of these drivers on forest fire incidence. Our results showed that the density of impervious surfaces, density of cropland, mean monthly precipitation, mean monthly temperature, density of primary gross domestic product, annual monthly average relative humidity, and annual monthly average precipitation at the county level, prefecture-level city and climatic levels had significant multilevel associations with forest fire incidence in the Changbai Mountain area. These findings will effectively support the development of forest fire administrative policies for specific regions.

Carbon isotope values for grasses in Madagascar's Central Highlands establish baselines for historical and paleoecological research

Societal Impact StatementGrasses are significant drivers of fires and are the primary food source for cattle in Madagascar's Central Highlands. However, their extent and importance to animals and people in the past remain poorly understood. Clarifying the history of Malagasy grasslands is necessary for building climate resilient food systems and supporting carbon stores that also conserve biodiversity. We generated chemical data for grasses that grow in open habitats in central Madagascar, which will help improve our understanding of the ecological and economic importance of modern grassy ecosystems, reconstruct the regional history of grasses, and anticipate how vegetation may respond to changing climate and rising atmospheric carbon dioxide levels.Summary Stable carbon isotope (δ13C) data for Malagasy grasses are needed to establish expected values for C3 and C4 grasses from particular regions in Madagascar, and possible differences among different grass lineages, or species with different distributions or adaptations. These data, in turn, may help inform how widespread grasses were in the past, and the importance of grasses to endemic and domesticated animals as well as people over time. We analysed both δ13C and weight %C:N from 63 Poaceae species that grow in open grassy biomes in Madagascar's Central Highlands and explored how these values relate to multiple variables, including encounter frequency, distribution, lineage, adaptations to grazing and fire and the typical floral assemblage in which each species occurs. Of the species sampled, 56 are C4 and seven are C3. There are no differences in δ13C or weight %C:N among either C3 or C4 species with different distributions or adaptations, from different assemblages, or that are frequently or infrequently encountered. However, there are differences in both δ13C and weight %C:N among C4 lineages, and the single C3 arundinoid (Styppeiochloa hitchcockii) has larger weight %C:N than C3 Paniceae. Our results provide a foundation for evaluating reliance on C4 resources by people, as well as domesticated and endemic animals both today and in the past. We encourage gathering additional comparative data for co‐occurring individual plants from the same open grassy biome localities, as well as other species, habitats and regions in Madagascar.

Climate and fire drivers of forest composition and openness in the Changbai Mountains since the Late Glacial

Ongoing climate changes have a direct impact on forest growth; they also affect natural fire regimes, with further implications for forest composition. Understanding of how these will affect forests on decadal-to-centennial timescales is limited. Here we use reconstructions of past vegetation, fire regimes and climate during the Holocene to examine the relative importance of changes in climate and fire regimes for the abundance of key tree species in northeastern China. We reconstructed vegetation changes and fire regimes based on pollen and charcoal records from Gushantun peatland. We then used generalized linear modelling to investigate the impact of reconstructed changes in summer temperature, annual precipitation, background levels of fire, fire frequency and fire magnitude to identify the drivers of decadal-to-centennial changes in forest openness and composition. Changes in climate and fire regimes have independent impacts on the abundance of the key tree taxa. Climate variables are generally more important than fire variables in determining the abundance of individual taxa. Precipitation is the only determinant of forest openness, but summer temperature is more important than precipitation for individual tree taxa with warmer summers causing a decrease in cold-tolerant conifers and an increase in warmth-demanding broadleaved trees. Both background level and fire frequency have negative relationships with the abundance of most tree taxa; only Pinus increases as fire frequency increases. The magnitude of individual fires does not have a significant impact on species abundance on this timescale. Both climate and fire regime characteristics must be considered to understand changes in forest composition on the decadal-to-centennial timescale. There are differences, both in sign and magnitude, in the response of individual tree species to individual drivers.

Plant hydraulic modelling of leaf and canopy fuel moisture content reveals increasing vulnerability of a Mediterranean forest to wildfires under extreme drought.

Fuel moisture content (FMC) is a crucial driver of forest fires in many regions world-wide. Yet, the dynamics of FMC in forest canopies as well as their physiological and environmental determinants remain poorly understood, especially under extreme drought. We embedded a FMC module in the trait-based, plant-hydraulic SurEau-Ecos model to provide innovative process-based predictions of leaf live fuel moisture content (LFMC) and canopy fuel moisture content (CFMC) based on leaf water potential ( ). SurEau-Ecos-FMC relies on pressure-volume (p-v) curves to simulate LFMC and vulnerability curves to cavitation to simulate foliage mortality. SurEau-Ecos-FMC accurately reproduced and LFMC dynamics as well as the occurrence of foliage mortality in a Mediterranean Quercus ilex forest. Several traits related to water use (leaf area index, available soil water, and transpiration regulation), vulnerability to cavitation, and p-v curves (full turgor osmotic potential) had the greatest influence on LFMC and CFMC dynamics. As the climate gets drier, our results showed that drought-induced foliage mortality is expected to increase, thereby significantly decreasing CFMC. Our results represent an important advance in our capacity to understand and predict the sensitivity of forests to wildfires.

Multiple responses of bryophytes in a chronosequence of burnt areas in non-fire prone subtropical cloud forests

In non-fire prone ecosystems, like some subtropical humid forests, fire produces habitat destruction and intensifies land degradation by inducing changes in native species composition, soil properties and erosive processes. Bryophytes are key components of the Macaronesian laurel forests playing an important role in regulating water cycling and microclimate. Ecological and taxonomical bryophytes groups have distinct ecological and physiological requirements and may respond in a different way to the same fire events. Therefore, analysing post fire recovery of bryophyte communities represents a key step towards a better understanding of forest fire drivers and post fire management. We investigated how species richness and composition of different ecological and taxonomical bryophyte groups varied in 1158 samples within a fire chronosequence from 5 to 57 years in the best-preserved laurel forest from Canary Islands (Garajonay National Park) analysing communities in terms of differences with comparable surrounding old growth unburnt stands. Epiphyte, terricolous and saxicolous bryophytes were sampled at each plot and the influence of the time since fire was analyzed together with environmental variables (temperature, precipitation, mist precipitation and elevation) and forest structure variables. Our results indicate that there is no general pattern of post fire recolonization, as recolonization varies depending on the ecological and phylogenetic groups considered. Climate and forest structure play an important role in post-fire recolonization, such that time since fire is not the most important variable influencing richness and composition. The results increase the understanding of the processes that shape compositional patterns in groups with high dispersal capacities and high microclimate dependence, such as mosses and liverworts.

Effects of Forest Fire Prevention Policies on Probability and Drivers of Forest Fires in the Boreal Forests of China during Different Periods

Fire prevention policies during different periods may lead to changes in the drivers of forest fires. Here, we use historical fire data and apply the boosted regression tree (BRT) model to analyze the spatial patterns and drivers of forest fires in the boreal forests of China from 1981 to 2020 (40 years). We divided the fire data into four periods using the old and new Chinese Forest Fire Regulations as a dividing line. Our objectives here were: to explore the influence of key historical events on the drivers of forest fires in northern China, establish a probability model of forest fire occurrence, and draw a probability map of forest fire occurrence and a fire risk zone map, so as to interpret the differences in the drivers of forest fires and fire risk changes over different periods. The results show that: (1) The model results from 1981 to 2020 (all years) did not improve between 2009 and 2020 (the most recent period), indicating the importance of choosing the appropriate modeling time series length and incorporating key historical events in future forest fire modeling; (2) Climate factors are a dominant factor affecting the occurrence of forest fires during different periods. In contrast with previous research, we found that here, it is particularly important to pay attention to the relevant indicators of the autumn fire prevention period (average surface temperature, sunshine hours) in the year before the fire occurrence. In addition, the altitude and the location of watchtowers were considered to have a significant effect on the occurrence of forest fires in the study area. (3) The medium and high fire risk areas in our three chosen time periods (1981–14 March 1988; 15 March 1988–2008; 2009–2020) have changed significantly. Fire risks were higher in the east and southeast areas of the study area in all periods. The northern primeval forest area had fewer medium-risk areas before the new and old regulations were formulated, but the medium-risk areas increased significantly after the old regulations were revised. Our study will help understand the drivers and fire risk distribution of forest fires in the boreal forests of China under the influence of history and will help decision-makers optimize fire management strategies to reduce potential fire risks.

Predictive model of spatial scale of forest fire driving factors: a case study of Yunnan Province, China

Forest fires are among the major natural disasters that destroy the balance of forest ecosystems. The construction of a forest fire prediction model to investigate the driving mechanism of fire drivers on forest fires can help reveal the mechanism of forest fire occurrence and its risk, and thus contribute to the prevention and control of forest fires. However, previous studies on the mechanisms of forest fire drivers have not considered the effect of differences in spatial scale of action of forest fire drivers on the predicted effect. Therefore, the present study proposes a spatial prediction model of forest fires that considers the spatial scale effect of forest fire drivers to predict forest fire risk. First, based on historical forest fire data and geographic environmental data in the Yunnan Province, geographically weighted logistic regression (GWLR) was used to determine the forest fire drivers and to estimate the probability of forest fire occurrence at locations where fire observations are absent. Then, multi-scale geographically weighted regression (MGWR) was used to explore the spatial scales of action of different drivers on forest fires. The results show that meteorological factors such as relative humidity, air temperature, air pressure, sunshine hours, daily precipitation, wind speed, topographic factors such as elevation, slope, and aspect, anthropogenic factors such as population density and road network, as well as vegetation type, were significantly correlated with forest fires; thus, they are identified as important factors influencing occurrence of forest fires in the Yunnan Province. The MGWR model regression results show that the role of different forest fire drivers on forest fire occurrence has spatial scale differences. The spatial scale of drivers such as altitude, aspect, wind speed, temperature, slope, and distance from the road to the fire point was larger and their spatial influence was relatively stable, with spatial heterogeneity having less influence on the model evaluation results. The spatial scale of drivers such as relative humidity, sunshine, air pressure, precipitation, population density, and vegetation type were smaller, and spatial heterogeneity had a more obvious influence on the model evaluation results. This study provides a reference for selecting drivers and evaluating their spatial scale effects to construct predictive regional forest fire models.

Forest Fire Occurrence Prediction in China Based on Machine Learning Methods

Forest fires may have devastating consequences for the environment and for human lives. The prediction of forest fires is vital for preventing their occurrence. Currently, there are fewer studies on the prediction of forest fires over longer time scales in China. This is due to the difficulty of forecasting forest fires. There are many factors that have an impact on the occurrence of forest fires. The specific contribution of each factor to the occurrence of forest fires is not clear when using conventional analyses. In this study, we leveraged the excellent performance of artificial intelligence algorithms in fusing data from multiple sources (e.g., fire hotspots, meteorological conditions, terrain, vegetation, and socioeconomic data collected from 2003 to 2016). We have tested several algorithms and, finally, four algorithms were selected for formal data processing. There were an artificial neural network, a radial basis function network, a support-vector machine, and a random forest to identify thirteen major drivers of forest fires in China. The models were evaluated using the five performance indicators of accuracy, precision, recall, f1 value, and area under the curve. We obtained the probability of forest fire occurrence in each province of China using the optimal model. Moreover, the spatial distribution of high-to-low forest fire-prone areas was mapped. The results showed that the prediction accuracies of the four forest fire prediction models were between 75.8% and 89.2%, and the area under the curve (AUC) values were between 0.840 and 0.960. The random forest model had the highest accuracy (89.2%) and AUC value (0.96). It was determined as the best performance model in this study. The prediction results indicate that the areas with high incidences of forest fires are mainly concentrated in north-eastern China (Heilongjiang Province and northern Inner Mongolia Autonomous Region) and south-eastern China (including Fujian Province and Jiangxi Province). In areas at high risk of forest fire, management departments should improve forest fire prevention and control by establishing watch towers and using other monitoring equipment. This study helped in understanding the main drivers of forest fires in China over the period between 2003 and 2016, and determined the best performance model. The spatial distribution of high-to-low forest fire-prone areas maps were produced in order to depict the comprehensive views of China’s forest fire risks in each province. They were expected to form a scientific basis for helping the decision-making of China’s forest fire prevention authorities.

Targeted land management strategies could halve peatland fire occurrences in Central Kalimantan, Indonesia

Indonesian peatlands and their large carbon stores are under threat from recurrent large-scale fires driven by anthropogenic ecosystem degradation. Although the key drivers of peatland fires are known, a holistic methodology for assessing the potential of fire mitigation strategies is lacking. Here, we use machine learning (convolutional neural network) to develop a model capable of recreating historic fire observations based on pre-fire season parameters. Using this model, we test multiple land management and peatland restoration scenarios and quantify the associated potential for fire reduction. We estimate that converting heavily degraded swamp shrubland areas to swamp forest or plantations can reduce fires occurrence by approximately 40% or 55%, respectively. Blocking all but major canals to restore these degraded areas to swamp forest may reduce fire occurrence by 70%. Our findings suggest that effective land management strategies can influence fire regimes and substantially reduce carbon emissions associated with peatland fires, in addition to enabling sustainable management of these important ecosystems.

Exploring key drivers of forest fires in the Mole National Park of Ghana using geospatial tools

Exploring key drivers of forest fires in the Mole National Park of Ghana using geospatial tools

Fire Dynamics of the Bolivian Amazon

This study identifies the spatial and temporal trends, as well as the drivers, of fire dynamics in the Bolivian Amazon basin. Bolivia ranks in the top ten countries in terms of total annual burnt, with fires affecting an estimated 2.3 million hectares of forest in 2020. However, in comparison to the Brazilian Amazon, there has been little research into the fire regime in Bolivia. The sparse research and the limited literature on the subject indicate that fire activity is higher in the Bolivian Amazon basin’s dry forests and flooded savanna zones, and that agriculture and drought are the primary causes of fire activity. In this study, trend analysis and emerging hotspot analysis are deployed to identify the spatial and temporal patterns of fire activity and boosted regression tree models to identify the drivers of forest fire within each ecoregion of the Bolivian Amazon basin. Comparable to most of the Brazilian literature, this study finds that fire activity and fire season length is higher in the flooded Beni Savanna, and Chiquitano seasonally dry tropical forests than in the Bolivian Amazon ecoregion. This study also identifies moisture stress and human activity as the main drivers of fire dynamics within the region. It is intended that this research will offer a foundation for future research and conservation activities aimed at better understanding the fire regime of the Bolivian Amazon basin.

Editorial: Fire regimes in desert ecosystems: Drivers, impacts and changes

Although not commonly associated with fire, many desert ecosystems across the globe do occasionally burn, and there is evidence that fire incidences are increasing, leading to altered fire regimes in this biome. The increased prevalence of megafires (wildfires >10,000 ha in size) in most global biomes is linked to climate change (Linley et al. 2022), although those occurring in arid regions have received far less attention. Understanding the drivers of desert fires, from climate to landscape patterns of hydrology and soil, and how these may be changing in the face of anthropogenic pressures, such as invasive species, livestock grazing, and global climate change, is imperative. This Research Topic has published nine papers addressing these drivers, how they have changed, and their impacts on desert biodiversity. The various papers in this Research Topic have demonstrated that shifting fire drivers are leading to 96 altered fire regimes in many desert ecosystems, and that we need to be particularly concerned with 97 synergistic effects of these drivers, such as climate changes interacting with invasive grasses to 98 produce more frequent, hotter, and larger wildfires. The impending threats of changing fire regimes 99 on biodiversity requires an enhanced research agenda, particularly in desert regions outside of 100 southwestern USA and inland Australia (where all the studies in this RT were based). 101

Global and Regional Trends and Drivers of Fire Under Climate Change

AbstractRecent wildfire outbreaks around the world have prompted concern that climate change is increasing fire incidence, threatening human livelihood and biodiversity, and perpetuating climate change. Here, we review current understanding of the impacts of climate change on fire weather (weather conditions conducive to the ignition and spread of wildfires) and the consequences for regional fire activity as mediated by a range of other bioclimatic factors (including vegetation biogeography, productivity and lightning) and human factors (including ignition, suppression, and land use). Through supplemental analyses, we present a stocktake of regional trends in fire weather and burned area (BA) during recent decades, and we examine how fire activity relates to its bioclimatic and human drivers. Fire weather controls the annual timing of fires in most world regions and also drives inter‐annual variability in BA in the Mediterranean, the Pacific US and high latitude forests. Increases in the frequency and extremity of fire weather have been globally pervasive due to climate change during 1979–2019, meaning that landscapes are primed to burn more frequently. Correspondingly, increases in BA of ∼50% or higher have been seen in some extratropical forest ecoregions including in the Pacific US and high‐latitude forests during 2001–2019, though interannual variability remains large in these regions. Nonetheless, other bioclimatic and human factors can override the relationship between BA and fire weather. For example, BA in savannahs relates more strongly to patterns of fuel production or to the fragmentation of naturally fire‐prone landscapes by agriculture. Similarly, BA trends in tropical forests relate more strongly to deforestation rates and forest degradation than to changing fire weather. Overall, BA has reduced by 27% globally in the past two decades, due in large part to a decline in BA in African savannahs. According to climate models, the prevalence and extremity of fire weather has already emerged beyond its pre‐industrial variability in the Mediterranean due to climate change, and emergence will become increasingly widespread at additional levels of warming. Moreover, several of the major wildfires experienced in recent years, including the Australian bushfires of 2019/2020, have occurred amidst fire weather conditions that were considerably more likely due to climate change. Current fire models incompletely reproduce the observed spatial patterns of BA based on their existing representations of the relationships between fire and its bioclimatic and human controls, and historical trends in BA also vary considerably across models. Advances in the observation of fire and understanding of its controlling factors are supporting the addition or optimization of a range of processes in models. Overall, climate change is exerting a pervasive upwards pressure on fire globally by increasing the frequency and intensity of fire weather, and this upwards pressure will escalate with each increment of global warming. Improvements to fire models and a better understanding of the interactions between climate, climate extremes, humans and fire are required to predict future fire activity and to mitigate against its consequences.

Wild, tamed, and domesticated: Three fire macroregimes for global pyrogeography in the Anthropocene.

Climate and natural vegetation dynamics are key drivers of global vegetation fire, but anthropogenic burning now prevails over vast areas of the planet. Fire regime classification and mapping may contribute towards improved understanding of relationships between those fire drivers. We used 15 years of daily active fire data from the MODIS fire product (MCD14ML, collection 6) to create global maps of six fire descriptors (incidence, size inequality, season length, interannual variability, intensity, and fire season modality). Using multiple correspondence analysis (MCA) and hierarchical agglomerative clustering, we identified three fire macroregimes: Wild, Tamed, and Domesticated, each of which splitting into prototypical and transitional regimes. Interpretation of the six fire regimes in terms of their main drivers relied on the global maps of anthromes and Köppen climate types. The analysis yielded a two-dimensional space where the principal dimension of variability is primarily defined by interannual variability in fire activity and fire season length, and the secondary axis is based mainly on fire incidence. The Wild fire macroregime occurs mostly in cold wildlands, where burning is sporadic and fire seasons are short. Tamed fires predominate in seasonally dry tropical rangelands and croplands with high fire incidence. Domesticated fires are characteristic of humid, warm temperate and tropical croplands and villages with low fire incidence. The Tamed and Domesticated fire macroregimes, representing managed burning, account for 86% of all active fires in our dataset and for 70% of the global burnable area. Fourteen percent of active fires were found in the cold wildlands, and in the rangelands and forests of steppe and desert climates of the Wild macroregime. These results highlight the extent of human control over global pyrogeography in the Anthropocene.

Multi-century spatiotemporal patterns of fire history in black pine forests, Turkey

In this study, we aimed to use tree-ring based fire reconstruction to understand the spatiotemporal patterns of past fires in different climate types of western Anatolia. We collected fire scarred wood samples from living trees as wedges and remnant woods from ten sites along a transect that represents a continental to Mediterranean climate gradient. We determined fire years and assigned seasonality of fires based on the intraring position of the fire scars. We calculated fire statistics and analysed fire-climate relationships. Breakpoints in our Anatolian regional fire chronology were estimated to determine the regime shifts. A decrease in fire frequency was recorded at most of the sites after the end of the 19th and the beginning of the 20th century. We observed two critical fire regime shift periods. The period between 1853 and 1934 is characterized by highly frequent (a total of 82 fires) and simultaneous fires occurring in multiple sites and this period overlapped with the longest and most severe drought period of the past 550 years. The fire frequency decline after 1934 coincided with the period of the first forest protection law in 1937. Dry, as well as prior wet conditions were main drivers of fires in the black pine forests in western Anatolia. We observed a decrease in fire frequency in the late 19th and early 20th centuries due to fire suppression activities. Continued fire suppression activities may cause fuel accumulation and pose a risk for more intense fires and thus a paradox for forests in the future. Based on future climate projections, we will face prolonged fire seasons as a consequence of increasing drought frequency, which may shift the fire regime from surface to crown fires with the accumulation of combustible material in the understory in black pine forests.

Study of Driving Factors Using Machine Learning to Determine the Effect of Topography, Climate, and Fuel on Wildfire in Pakistan

As the climate changes with the population expansion in Pakistan, wildfires are becoming more threatening. The goal of this study was to understand fire trends which might help to improve wildland management and reduction in wildfire risk in Pakistan. Using descriptive analyses, we investigated the spatiotemporal trends and causes of wildfire in the 2001–2020 period. Optimized machine learning (ML) models were incorporated using variables representing potential fire drivers, such as weather, topography, and fuel, which includes vegetation, soil, and socioeconomic data. The majority of fires occurred in the last 5 years, with winter being the most prevalent season in coastal regions. ML models such as RF outperformed others and correctly predicted fire occurrence (AUC values of 0.84–0.93). Elevation, population, specific humidity, vapor pressure, and NDVI were all key factors; however, their contributions varied depending on locational clusters and seasons. The percentage shares of climatic conditions, fuel, and topographical variables at the country level were 55.2%, 31.8%, and 12.8%, respectively. This study identified the probable driving factors of Pakistan wildfires, as well as the probability of fire occurrences across the country. The analytical approach, as well as the findings and conclusions reached, can be very useful to policymakers, environmentalists, and climate change researchers, among others, and may help Pakistan improve its wildfire management and mitigation.

The influence of bioclimatic and topographic variables on grassland fire occurrence within an urbanized landscape

Unplanned veldfires (or wildfires) characterize vegetation landscapes and offer a range of ecological benefits that promote, among others, the health of the grasslands and other fire-adapted ecosystems. However, in urbanized areas, uncontroled fires are often a threat to property, life, the environment, and economy. The eThekwini Municipal area, a global biodiversity hot-spot experiences frequent unplanned veld fires that threatens the valuable remnant grasslands. This necessitates an understanding of key drivers to fire occurrence as a first step towards the remnant grasslands sustainability. In this study, the probability of fire occurrence within the study area was determined using the Near Real-Time (NRT) MODIS Collection 6 Active Fire Data, topographic and bioclimatic variables within the Maximum Entropy (Maxent) environment. The predictor variables were assessed using jackknife analysis, percentage contribution, and Area Under Curve (AUC). The results showed that the mean temperature of the coldest quarter (33%), isothermality (12.3%), elevation (8.9%), and precipitation of the warmest month (8.8%) were the most influential predictor variables affecting fire occurrence within the study area. The Area Under Curve (AUC) values for training and test data-sets were 0.728 and 0.716 respectively, indicating good accuracy for the fire occurrence probability modeling. The study concludes that the Maxent modeling algorithm is suitable for determining fire occurrence and identifying key topographic and bioclimatic fire drivers within an urban landscape. These results are valuable in informing the protection and conservation of urban ecological systems, useful for the provision of urban ecosystem goods and services.

The role of decomposer communities in managing surface fuels: a neglected ecosystem service

Surface fuel loads are a key driver of forest fires and the target of hazard reduction burns to reduce fire risk. However, the role of biota in decomposition, or feedbacks between fire and decomposer communities are rarely considered. We review the evidence that decomposer organisms play an important role in surface fuel regulation and how this role is affected by fire. First, we outline the contribution of decomposer organisms to the breakdown of surface fuels. Next, we consider the three distinct phases through which fire regulates decomposer communities and how this may affect decomposition and future fire regimes. Finally, we consider interactions between global change and decomposer–fire feedbacks and the implications for fire management. Evidence indicates that decomposer organisms are important in regulating surface fuels and we propose that the biological basis and dynamic nature of fuel load control require greater attention. This includes better understanding of functional redundancy among decomposer organisms, the impacts of global change on the biota that drive decomposition and the factors that limit decomposer persistence and recolonisation following fires. By filling these knowledge gaps, we will be better armed to conserve and manage these functionally critical taxa in fire-prone ecosystems in a changing world.

Forest fire and its key drivers in the tropical forests of northern Vietnam

Fire increasingly threatens tropical forests in northern Vietnam as climate changes and human population grows. Understanding fire occurrence patterns may support more effective forest management and reduce fire risk. We investigated spatiotemporal patterns and drivers of wildfire across three provinces in northern Vietnam and assessed the effectiveness of the Modified Nesterov index (MNI) fire danger rating system. We explored fire occurrence and size within and between years and forest types using descriptive analyses and developed spatiotemporal Maximum Entropy (Maxent) models incorporating variables representing potential drivers of fire, including weather, fuel, topography and human activity. Most fires occurred late in the dry season and fires were most common in natural forest. Maxent models successfully predicted fire occurrence (area under the receiver operating characteristic curve (AUC) values 0.67–0.79). While the contributions of drivers varied among provinces, MNI, temperature, elevation and distance to road were consistently important. The model for combined provinces showed that fire probability was greater under higher temperature and MNI, in areas with lower population, farther from roads, at higher elevations and in natural forests. This study suggests that an assessment integrating multiple drivers better predicts fire occurrence than a system based on weather alone and may support improved fire management and education in northern Vietnam.

Using Multilayer Perceptron to Predict Forest Fires in Jiangxi Province, Southeast China

The forest fire occurrence prediction model is a very useful tool for preventing and extinguishing forest fires. The determination of forest fire drivers is important for establishing a high‐precision forest fire prediction model. In this paper, we studied the relative influence of different types of factors on forest fire occurrence in forest areas of Jiangxi Province. Several models, i.e., Multilayer perceptron (MLP), Logistic, and Support vector machine (SVM), are used to predict the occurrence of forest fires. Through modeling and analysis of forest fire data from 2010 to 2016 years, we found that climatic and topographic are influential factors in the model of forest fire occurrence in Jiangxi Province. Subsequently, we established the MLP occurrence model based on the significant factors after the variable screening. Using ROC plots to compare the effects of the three models, MLP scored 0.984, which was higher than Logistic of 0.933 and SVM of 0.974. For the independent validation set of 2017‐2018, an accuracy of 91.73% was also achieved. Therefore, the multilayer perceptron is well suited for the prediction of forest fires in Jiangxi Province. Based on the prediction results, a fire risk level map of Jiangxi Province was produced. Finally, we analyzed the changes in forest fire quantity under climate change, which can be helpful for fire prevention and suppression of forest fires.