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Biology subjects

Simioni, G.

Publications and source records attributed to Simioni, G..

3 recordsLinked to original sources

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↗

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↗

Beyond forest succession: a gap model to study ecosystem functioning and tree community composition under climate change

Climate change impacts forest functioning and dynamics, and large uncertainties remain regarding the interactions between species composition, demographic processes, and environmental drivers. There are few robust tools available to link these processes, which precludes accurate projections and recommendations for long-term forest management. Forest gap-models present a balance between complexity and generality and are widely used in predictive forest ecology. However, their relevance to tackle questions about the links between species composition, climate and forest functioning is unclear. In this regard, demonstrating the ability of gap-models to predict the growth of forest stands at the annual time scale - representing a sensitive and integrated signal of tree functioning and mortality risk - appears as a fundamental step. In this study, we aimed at assessing the ability of a gap-model to accurately predict forest growth in the short-term and potential community composition in the long-term, across a wide range of species and environmental conditions. To do so, we present the gap-model ForCEEPS, calibrated using an original parameterization procedure for the main tree species in France. ForCEEPS was shown to satisfactorily predict forest annual growth (averaged over a few years) at the plot level from mountain to Mediterranean climates, regardless the species. Such an accuracy was not gained at the cost of losing precision for long-term predictions, as the model showed a strong ability to predict potential community composition along a gradient of sites with contrasted conditions. The mechanistic relevance of ForCEEPS parameterization was explored by showing the congruence between the values of key model parameter and species functional traits. We further showed that accounting for the spatial configuration of crowns within forest stands, the effects of climatic constraints and the variability of shade tolerances in the species community are all crucial to better predict short-term productivity with gap-models. The dual ability of predicting short-term functioning and long-term community composition, as well as the balance between generality and realism (i.e., predicting accuracy) of the new generation of gap-models may open great perspectives for the exploration of the biodiversity-ecosystem functioning relationships, species coexistence mechanisms, and the impacts of climate change on forest ecosystems.

ecology↗