Isohydricity and hydraulic isolation explain reduced hydraulic failure risk in an experimental tree species mixture.

Species mixture is promoted as a crucial management option to adapt forests to climate change. However, there is little consensus on how tree diversity affects tree water stress, and the underlying mechanisms remain elusive. By using a greenhouse experiment and a soil-plant-atmosphere hydraulic model, we explored whether and why mixing the isohydric Aleppo pine (Pinus halepensis, drought avoidant) and the anisohydric holm oak (Quercus ilex, drought tolerant) affects tree water stress during extreme drought. Our experiment showed that the intimate mixture strongly alleviated Q. ilex water stress while it marginally impacted P. halepensis water stress. Three mechanistic explanations for this pattern are supported by our modelling analysis. First, the difference in stomatal regulation between species allowed Q. ilex trees to benefit from additional soil water in mixture, thereby maintaining higher water potentials and sustaining gas exchange. By contrast, P. halepensis exhibited earlier water stress and stomatal regulation. Second, P. halepensis trees showed stable water potential during drought, although soil water potential strongly decreased, even when grown in a mixture. Model simulations suggested that hydraulic isolation of the root from the soil associated with decreased leaf cuticular conductance was a plausible explanation for this pattern. Third, the higher predawn water potentials for a given soil water potential observed for Q. ilex in mixture can - according to model simulations - be explained by increased soil-to-root conductance, resulting from higher fine root length. This study brings insights into the mechanisms involved in improved drought resistance of mixed species forests.

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 ( ψ Leaf ). 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 ψ Leaf 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.

Integrating plant physiology into simulation of fire behavior and effects.

Wildfires are a global crisis, but current fire models fail to capture vegetation response to changing climate. With drought and elevated temperature increasing the importance of vegetation dynamics to fire behavior, and the advent of next generation models capable of capturing increasingly complex physical processes, we provide a renewed focus on representation of woody vegetation in fire models. Currently, the most advanced representations of fire behavior and biophysical fire effects are found in distinct classes of fine-scale models and do not capture variation in live fuel (i.e. living plant) properties. We demonstrate that plant water and carbon dynamics, which influence combustion and heat transfer into the plant and often dictate plant survival, provide the mechanistic linkage between fire behavior and effects. Our conceptual framework linking remotely sensed estimates of plant water and carbon to fine-scale models of fire behavior and effects could be a critical first step toward improving the fidelity of the coarse scale models that are now relied upon for global fire forecasting. This process-based approach will be essential to capturing the influence of physiological responses to drought and warming on live fuel conditions, strengthening the science needed to guide fire managers in an uncertain future.

Prediction of regional wildfire activity in the probabilistic Bayesian framework of Firelihood.

Modeling wildfire activity is crucial for informing science-based risk management and understanding the spatiotemporal dynamics of fire-prone ecosystems worldwide. Models help disentangle the relative influences of different factors, understand wildfire predictability, and provide insights into specific events. Here, we develop Firelihood, a two-component, Bayesian, hierarchically structured, probabilistic model of daily fire activity, which is modeled as the outcome of a marked point process: individual fires are the points (occurrence component), and fire sizes are the marks (size component). The space-time Poisson model for occurrence is adjusted to gridded fire counts using the integrated nested Laplace approximation (INLA) combined with the stochastic partial differential equation (SPDE) approach. The size model is based on piecewise-estimated Pareto and generalized Pareto distributions, adjusted with INLA. The Fire Weather Index (FWI) and forest area are the main explanatory variables. Temporal and spatial residuals are included to improve the consistency of the relationship between weather and fire occurrence. The posterior distribution of the Bayesian model provided 1,000 replications of fire activity that were compared with observations at various temporal and spatial scales in Mediterranean France. The number of fires larger than 1 ha across the region was coarsely reproduced at the daily scale, and was more accurately predicted on a weekly basis or longer. The regional weekly total number of larger fires (10-100 ha) was predicted as well, but the accuracy degraded with size, as the model uncertainty increased with event rareness. Local predictions of fire numbers or burned areas also required a longer aggregation period to maintain model accuracy. The estimation of fires larger than 1 ha was also consistent with observations during the extreme fire season of the 2003 unprecedented heat wave, but the model systematically underrepresented large fires and burned areas, which suggests that the FWI does not consistently rate the actual danger of large fire occurrence during heat waves. Firelihood enabled a novel analysis of the stochasticity underlying fire hazard, and offers a variety of applications, including fire hazard predictions for management and projections in the context of climate change.

Increased likelihood of heat-induced large wildfires in the Mediterranean Basin.

Wildfire activity is expected to increase across the Mediterranean Basin because of climate change. However, the effects of future climate change on the combinations of atmospheric conditions that promote wildfire activity remain largely unknown. Using a fire-weather based classification of wildfires, we show that future climate scenarios point to an increase in the frequency of two heat-induced fire-weather types that have been related to the largest wildfires in recent years. Heat-induced fire-weather types are characterized by compound dry and warm conditions occurring during summer heatwaves, either under moderate (heatwave type) or intense (hot drought type) drought. The frequency of heat-induced fire-weather is projected to increase by 14% by the end of the century (2071-2100) under the RCP4.5 scenario, and by 30% under the RCP8.5, suggesting that the frequency and extent of large wildfires will increase throughout the Mediterranean Basin.

[Effects of fire recurrence on fire behaviour in cork oak woodlands (Quercus suber L.) and Mediterranean shrublands over the last fifty years]. / Effets de la récurrence des incendies sur le comportement du feu dans des suberaies (Quercus suber L.) et maquis méditerranéens sur les cinquante dernières années.

Past fire recurrence impacts the vegetation structure, and it is consequently hypothesized to alter its future fire behaviour. We examined the fire behaviour in shrubland-forest mosaics of southeastern France, which were organized along a range of fire frequency (0 to 3-4 fires along the past 50 years) and had different time intervals between fires. The mosaic was dominated by Quercus suber L. and Erica-Cistus shrubland communities. We described the vegetation structure through measurements of tree height, base of tree crown or shrub layer, mean diameter, cover, plant water content and bulk density. We used the physical model Firetec to simulate the fire behaviour. Fire intensity, fire spread, plant water content and biomass loss varied significantly according to fire recurrence and vegetation structure, mainly linked to the time since the last fire, then the number of fires. These results confirm that past fire recurrence affects future fire behaviour, with multi-layered vegetation (particularly high shrublands) producing more intense fires, contrary to submature Quercus woodlands that have not burnt since 1959 and that are unlikely to reburn. Further simulations, with more vegetation scenes according to shrub and canopy covers, will complete this study in order to discuss the fire propagation risk in heterogeneous vegetation, particularly in the Mediterranean area, with a view to a local management of these ecosystems.