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An amendment to this paper has been published and can be accessed via a link at the top of the paper.

Globe-LFMC is an extensive global database of live fuel moisture content (LFMC) measured from 1,383 sampling sites in 11 countries: Argentina, Australia, China, France, Italy, Senegal, Spain, South Africa, Tunisia, United Kingdom and the United States of America. The database contains 161,717 individual records based on in situ destructive samples used to measure LFMC, representing the amount of water in plant leaves per unit of dry matter. The primary goal of the database is to calibrate and validate remote sensing algorithms used to predict LFMC. However, this database is also relevant for the calibration and validation of dynamic global vegetation models, eco-physiological models of plant water stress as well as understanding the physiological drivers of spatiotemporal variation in LFMC at local, regional and global scales. Globe-LFMC should be useful for studying LFMC trends in response to environmental change and LFMC influence on wildfire occurrence, wildfire behavior, and overall vegetation health.

Dry lightning is a prevalent episodic natural ignition source for wildfires, particularly in remote regions where such fires can escalate into uncontrollable events, burning extensive areas. In this study, we aimed to understand the interplay of environmental, fuel, and geographical factors in evaluating the probability of fire initiation following dry lightning strikes in Tasmania, Australia. We integrated dry lightning, active fire records, and gridded data on fire weather, fuel, and topography into a binary classification framework for both fire‐initiating and non‐fire‐causing lightning strikes. Employing statistical and machine learning techniques, we quantified the likelihood of fire initiation due to dry lightning, with the resampled Random Forest model exhibiting notable performance with an ROC‐AUC value of 0.98. Our findings highlight how fuel characteristics and moisture content associated with particular vegetation types influence fire initiation and provide an objective approach for identifying susceptible regions of dry lightning ignitions, informing associated fire management responses. Plain Language Summary: Lightning strikes occurring with minimal rainfall, known as dry lightning strikes, often ignite wildfires in dry vegetation, especially in remote areas. These fires can remain undetected for hours to days and quickly grow out of control under favorable conditions, causing extensive damage and burning large areas. Our study investigated how environmental factors such as weather, terrain, and fuel variables (fuel load, fuel type, and fuel moisture) affect the probability of a fire starting after a dry lightning strike in Tasmania, Australia. We analyzed data on lightning strikes, active fires, topography, fuel (via vegetation and fire history), and weather conditions, to build advanced machine learning models to predict the chances of a fire started following a dry lightning strike. Our findings indicate a high probability of ignition in scenarios characterized by high fuel loads and low fuel moisture content, particularly in treeless vegetation types such as buttongrass sedgelands. This information can help fire authorities identify areas or times most at risk of dry lightning‐ignited wildfires, allowing for better fire prevention and response strategies. Key Points: Random Forest modeling accurately identified fires ignited by dry lightning strikes in TasmaniaWestern Tasmania exhibits heightened vulnerability to wildfires ignited by lightning strikesTreeless fuel types have higher risk than forests of dry lightning ignitions [ABSTRACT FROM AUTHOR]

This scientific commentary discusses the mechanisms of tree death caused by fire. The authors highlight the importance of understanding how trees respond to fire, especially as fire regimes continue to change due to climate change. They explore various hypotheses for fire-induced tree mortality, including immediate causes such as necrosis or consumption of leaves and meristems, as well as delayed causes such as carbon imbalance/starvation and hydraulic dysfunction. The authors present a recent study that focuses on how fire can cause hydraulic dysfunction in trees and discuss the potential mechanisms involved. They also emphasize the need for further research to fully understand the complex physiological responses of trees to fire. [Extracted from the article]

Aim: Increased tree mortality linked to droughts and fires is occurring across temperate regions globally. Vegetation recovery has been widely reported; however, less is known about how disturbance may alter forests structurally and functionally across environmental gradients. We examined whether dry forests growing on low‐fertility soils were more resilient to coupled extreme drought and severe fire owing to lower tree mortality rates, higher resprouting success and persistence of juveniles relative to wetter forests on more fertile soils. Location: Fire‐tolerant eucalypt forests of temperate southeastern Australia. Time period: 2020–2023. Major taxa studied: Eucalyptus, Corymbia, Angophora. Methods: Demographic surveys of tree mortality and regeneration in all combinations of dry/wet forest, fertile/less fertile substrates exposed to extreme drought and fire were conducted. We used Bayesian regression modelling to compare tree mortality, diameter, response traits, population structure and occurrence of fire scars between substrates/forest types. Results: Overall mortality (20%–33%) and topkill (34%–41%) were within historically reported ranges for various forests and soil types. However, we observed an atypical trend of increased mortality and topkill in the largest trees, particularly when they had structural damage from past fires. Trees in wet forests on more fertile soils had the highest levels of mortality. Numbers of persistent resprouting juveniles were highest in dry forests on low‐fertility soils. Dry forests growing on low‐fertility soils appear more resilient to compound disturbances due to lower rates of mortality and higher rates of juvenile persistence. Wet forests on more fertile soils may experience greater demographic change due to higher mortality of small and large trees. Main conclusions: Mesic forests on relatively fertile soils were found to be at relatively high risk of demographic change from compound disturbances. Combined, fire and drought are likely to reduce the number of large trees in affected areas, with consequences for forest carbon cycling and storage. [ABSTRACT FROM AUTHOR]

Understanding the biophysical limitations on forest carbon across diverse ecological regions is crucial for accurately assessing and managing forest carbon stocks. This study investigates the role of climate and disturbance on the spatial variation of two key forest carbon pools: aboveground carbon (AGC) and soil organic carbon (SOC). Using plot‐level carbon pool estimates from Nepal's national forest inventory and structural equation modelling, we explore the relationship of forest carbon stocks to broad‐scale climatic water and energy availability and fine‐scale terrain and disturbance. The forest AGC and SOC models explained 25% and 59% of the observed spatial variation in forest AGC and SOC, respectively. Among the evaluated variables, disturbance exhibited the strongest negative correlation with AGC, while the availability of climatic energy demonstrated the strongest negative correlation with SOC. Disturbances such as selective logging and firewood collection result in immediate forest carbon loss, while soil carbon changes take longer to respond. The lower decomposition rates in the high‐elevation region, due to lower temperatures, preserve organic matter and contribute to the high SOC stocks observed there. These results highlight the critical role of climate and disturbance regimes in shaping landscape patterns of forest carbon stocks. Understanding the underlying drivers of these patterns is crucial for forest carbon management and conservation across diverse ecological zones including the Central Himalayas. [ABSTRACT FROM AUTHOR]

Background: The moisture content of litter and woody debris is a key determinant of fire potential and fire behaviour. Obtaining reliable estimates of the moisture content of dead fine fuels (i.e. 1-h and 10-h fuels) is therefore a critical requirement for effective fire management. Aims: We evaluated and compared the performance of five simple models for fuel moisture content. The models belong to two separate classes: (1) exponential functions of the vapour pressure deficit; and (2) affine functions of the (weighted) difference between air temperature and relative humidity. Methods: Model performance is assessed using error and correlation statistics, calculated using cross validation, over four empirical datasets. Key results: Overall, the best performing models were the relaxed and generalised models based on the weighted difference between temperature and relative humidity. Conclusions: Simple functions of the difference between air temperature and relative humidity can perform as well as, if not better than exponential functions of vapour pressure deficit. However, it is important to note the limitations of all these models when applied to fuels with moisture contents <10%. Implications: The moisture content of fine dead fuels and woody debris can be reliably estimated using simple models that are amenable to easy application. Five models for the moisture content of fine dead fuels were evaluated using empirical datasets. Simple functions of the difference between air temperature and relative humidity were shown to perform as well as, if not better than exponential models based on vapour pressure deficit. [ABSTRACT FROM AUTHOR]

Spatially explicit predictions of fuel moisture content are crucial for quantifying fire danger indices and as inputs to fire behaviour models. Remotely sensed predictions of fuel moisture have typically focused on live fuels; but regional estimates of dead fuel moisture have been less common. Here we develop and test the spatial application of a recently developed dead fuel moisture model, which is based on the exponential decline of fine fuel moisture with increasing vapour pressure deficit (D). We first compare the performance of two existing approaches to predict D from satellite observations. We then use remotely sensed D, as well as D estimated from gridded daily weather observations, to predict dead fuel moisture. We calibrate and test the model at a woodland site in South East Australia, and then test the model at a range of sites in South East Australia and Southern California that vary in vegetation type, mean annual precipitation (129-1404mmyear-1) and leaf area index (0.1-5.7). We found that D modelled from remotely sensed land surface temperature performed slightly better than a model which also included total precipitable water (MAE

The moisture content of vegetation and litter (fuel moisture) is an important determinant of fire risk, and predictions of dead fine fuel moisture content (fuel with a diameter

"Least‐cost theory" posits that C3 plants should balance rates of photosynthetic water loss and carboxylation in relation to the relative acquisition and maintenance costs of resources required for these activities. Here we investigated the dependency of photosynthetic traits on climate and soil properties using a new Australia‐wide trait dataset spanning 528 species from 67 sites. We tested the hypotheses that plants on relatively cold or dry sites, or on relatively more fertile sites, would typically operate at greater CO2 drawdown (lower ratio of leaf internal to ambient CO2, Ci:Ca) during light‐saturated photosynthesis, and at higher leaf N per area (Narea) and higher carboxylation capacity (Vcmax 25) for a given rate of stomatal conductance to water vapour, gsw. These results would be indicative of plants having relatively higher water costs than nutrient costs. In general, our hypotheses were supported. Soil total phosphorus (P) concentration and (more weakly) soil pH exerted positive effects on the Narea–gsw and Vcmax 25–gsw slopes, and negative effects on Ci:Ca. The P effect strengthened when the effect of climate was removed via partial regression. We observed similar trends with increasing soil cation exchange capacity and clay content, which affect soil nutrient availability, and found that soil properties explained similar amounts of variation in the focal traits as climate did. Although climate typically explained more trait variation than soil did, together they explained up to 52% of variation in the slope relationships and soil properties explained up to 30% of the variation in individual traits. Soils influenced photosynthetic traits as well as their coordination. In particular, the influence of soil P likely reflects the Australia's geologically ancient low‐relief landscapes with highly leached soils. Least‐cost theory provides a valuable framework for understanding trade‐offs between resource costs and use in plants, including limiting soil nutrients. [ABSTRACT FROM AUTHOR]