Biology subjects

Ruffault, J.

Publications and source records attributed to Ruffault, J..

6 recordsLinked to original sources

bioRxiv2026

Rooting for water: Bridging Plant Hydraulics and Ecohydrology to Predict Drought Stress

Forests regulate climate and sustain biodiversity, but their resilience is increasingly threatened by drought-induced hydraulic failure, when xylem water transport is impaired by embolism. A critical but poorly constrained determinant of this risk largely due to limited observations of deep root water uptake is the amount of root-accessible (sub)soil water storage (SR), which governs land-atmosphere exchanges during prolonged dry periods. While ecohydrological theory has long suggested a balance between soil water storage in the rooting zone, vegetation growth and drought tolerance, this concept has not yet been integrated into predictive models of hydraulic failure. Here we propose a novel, process-based inversion framework that integrates ecohydrological optimality theory with principles of plant hydraulic to infer SR. Using the SurEau plant hydraulic model, we estimate the SR value that balances the costs of soil exploration with the avoidance of drought-induced hydraulic damage. Applied first to a well-instrumented Mediterranean Quercus ilex forest, the inferred SR closely matched independent neutron probe and eddy covariance measurements and accurately reproduced observed drought responses, including leaf water potential and sap flow dynamics. We then scaled the approach across European forests remotely-sensed data, to assess spatially explicit SR estimates and associated hydraulic failure risk. Across more than 20 species and sites, inferred SR and drought stress metrics aligned with field observations and outperformed estimates based on conventional soil databases or remote-sensing-only approaches. By linking plant physiology and ecohydrological theory within a scalable inversion framework, this approach improves predictions of forest drought risk under climate change.

ecology↗

bioRxiv2023

Functional diversity reduces the risk of hydraulic failure in tree mixtures through hydraulic disconnection

Forest ecosystems are increasingly threatened by anthropogenic pressures, especially by the increase in drought frequency and intensity. Tree species mixtures could improve resilience to diverse global anthropogenic pressures. However, there is still little consensus on how tree diversity affects water stress. Although some studies suggest that mixing species with different drought response strategies could be beneficial, the underlying mechanisms have seldom been identified. By combining a greenhouse experiment and a soil-plant-atmosphere hydraulic model, we explored whether mixing a drought avoidant (Pinus halepensis) and a drought tolerant (Quercus ilex) tree species could reduce plant water stress (defined as the risk of hydraulic failure) during extreme drought, compared to their respective monocultures. Our experiment showed that mixing species with divergent drought response strategies had a neutral effect on the drought-avoidant species and a positive effect on the drought-tolerant species. The model simulations further suggested that the beneficial effect of mixture on plant water stress during extreme drought was related to changes in the hydraulic connection of the plant from both the soil and the atmosphere. The ability of the drought-avoidant species to disconnect from the soil and the atmosphere limits its exposure to water stress, whereas the ability of the drought-tolerant species to increase its hydraulic connection to the soil lowers its hydraulic risk. This study brings a new insight on the mechanisms and traits combinations improving drought resistance in diversified forests and plantations, with important implications for forest management under climate change.

ecology↗

bioRxiv2020

Modelling live fuel moisture content at leaf and canopy scale under extreme drought using SurEau-Ecos, a lumped plant hydraulic model

O_LIWater content in living vegetation (or live fuel moisture content, LFMC), is increasingly recognized as a key factor linked to vegetation mortality and wildfire ignition and spread. Most often, empirical indices are used as surrogates for direct LFMC measurements. C_LIO_LIIn this paper, we explore the functional and ecophysiological drivers of LFMC during drought at the leaf and canopy scale using the SurEau-Ecos model, and a three years dataset of leaf and canopy scale measurements on a mature Quercus ilex forest, including an extreme drought. The model is based on forest hydrology and plant hydraulics and allows to simulate temporal variations of water potential and content at a daily time step. At leaf level, it simulates the relationship between water potential and water content by separating the apoplasm and the symplasm. Symplasm water content is modeled using the pressure volume curve theory, and apoplasm water content is modelled using the xylem vulnerability to cavitation. Fuel moisture content was upscaled to the canopy level by accounting for foliage mortality estimated from drought induced cavitation. C_LIO_LIThe model was parameterized either with site-measured traits or using a calibration procedure, and compared with water potential and LFMC measured at leaf level, and NDVI variation measured at canopy level and taken as a surrogate for foliage mortality. C_LIO_LIAt leaf level, LFMC prediction using measured hydraulic traits could be improved by considering year-to-year osmotic adjustments. At canopy level, foliage mortality due to drought induced cavitation was a key driver of LFMC decline during the most extreme drought. C_LIO_LIA sensitivity analysis showed that parameters driving soil water balance (leaf area index, soil water capacity, and regulation of transpiration) and parameters determining pressure volume curves are key traits driving LFMC dynamics at leaf level. At the canopy level, parameters that drives hydraulic failure were the most sensitive and included, both soil water balance parameters and hydraulic traits (the leaf vulnerability to cavitation) were the main drivers of LFMC decline during extreme drought. C_LIO_LIWe also showed that under normal historic weather conditions, most variation of LFMC are linked to reversible symplasm dehydration, however under future, hotter and dryer conditions, most variations are due to the decline canopy of LFMC driven by foliage mortality. C_LI

ecology↗

bioRxiv2020

Prediction of regional wildfire activity with a probabilistic Bayesian framework

O_LIModelling wildfire activity is crucial for informing science-based risk management and understanding fire-prone ecosystem functioning worldwide. Models also help to disentangle the relative roles of different factors, to understand wildfire predictability or to provide insights into specific events. C_LIO_LIHere, we develop a two-component Bayesian hierarchically-structured probabilistic model of daily fire activity, which are modelled as the outcome of a marked point process in which individual fires are the points (occurrence component) and the 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, also adjusted with INLA. The Fire Weather Index (FWI) and Forest Area are the main explanatory variables. Seasonal and spatial residuals as well as a post-2003 effect are included to improve the consistency of the relationship between climate and fire occurrence, in accordance with parsimonious criteria. C_LIO_LIA set of 1000 simulations of the posterior model of fire activity is evaluated at various temporal and spatial scales in Mediterranean France. The number of escaped fires ([≥]1ha) across the region can be coarsely reproduced at the daily scale, and is more accurately predicted on a weekly basis or longer. The regional weekly total number of larger fires (10 to 100 ha) can be predicted as well, but the accuracy decays with size, as the model uncertainty increases with event rareness. Local predictions of fire numbers or burnt areas likewise require a longer aggregation period to maintain model accuracy. C_LIO_LIRegarding the year 2003 -which was characterized by an extreme burnt area in France associated with a heat wave-, the estimation of the number of escaped fires was consistent with observations, but the model systematically underrepresents larger fires and burnt areas, which suggests that the FWI does not consistently rate the danger of large fire occurrence during heat waves. C_LIO_LIOur study sheds new light on the stochastic processes underlying fire hazard, and is promising for predicting and projecting future fire hazard in the context of climate change. C_LI

bioengineering↗

bioRxiv2020

SurEau.c : a mechanistic model of plant water relations under extreme drought

We describe the operating principle of the detailed version of the soil-plant-atmosphere model SurEau that allows, among other things, to predict the risk of hydraulic failure under extreme drought. It is based on the formalization of key physiological processes of plant response to water stress. The hydraulic functioning of the plant is at the core of this model, which focuses on both water flows and water pools using variable hydraulic conductances. The model considers the elementary flow of water from the soil to the atmosphere through different plant organs (roots, trunk branches, leaves and buds) that are described by their symplasm and their apoplasm compartments. Within each organ the flow of water between the apoplasm and the symplasm is also represented; as well as the flow outside the system, from the symplasm of each organ to the atmosphere, through the cuticular conductance. For each organ, the symplasm is described by a pressure volume curves and the apoplasm by the vulnerability curve to cavitation of the xylem. The model can thus compute the loss of conductance caused by cavitation, a leading mechanisms of plant desiccation and drought-induced mortality. Some example simulations are shown to illustrate how the model works.

plant biology↗

bioRxiv2020

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 changes on the combinations of atmospheric conditions that promote large wildfires remain largely unknown. Using a fire-weather based classification of wildfires, we show that future climate scenarios point to an increase in the frequency and severity of two heat-induced fire-weather types that have been responsible for a majority of record-breaking wildfire events. Heat-induced fire-weather types are characterized by compound dry warm conditions and occur in the summer during heatwaves, either under moderate (sudden heatwave type) or intense (hot drought type) drought. Heat-induced fire weather is projected to increase in frequency by 14% by the end of the century (2071-2100) under the RCP4.5 scenario, and by 30% under the RCP8.5. These findings suggest that the frequency and extent of large wildfires will increase throughout the Mediterranean Basin, with far-reaching impacts.

ecology↗