Biology subjects

De Caceres, M.

Publications and source records attributed to De Caceres, M..

4 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↗

bioRxiv2026

Loss of competitive strength in European conifer species under climate change

Climate change is expected to alter species assemblages by affecting the outcome of competition between species. Investigating processes of competition remains challenging particularly in tree communities, as they unfold over extensive spatio-temporal scales. Here, we developed a deep-learning approach to leverage a novel database of 135 million simulated local-scale tree responses to climate across continental Europe to investigate changes in the competitiveness of nine major tree species under different scenarios of climate change. Specifically, we trained a Deep Neural Network on local process model projections to investigate climate change effects on indicators of competitive strength and species dominance. We found decreasing competitive strength for all investigated evergreen coniferous species across their distribution, while major deciduous broadleaved species such as Quercus robur and Fagus sylvatica increased in competitiveness. Changes in tree species competition with climate differed locally, but most investigated species lost competitive strength at their warm range edges. As a consequence of these changes, up to 19% of Europes forests could experience a change in the dominant tree species until the end of the 21st century. Our results suggest a profound climate-induced reassembly of Europes forests and identify areas that may require specific attention in forest policy and management.

ecology↗

bioRxiv2024

Assessing the combined effects of forest management and climate change on carbon and water fluxes in European beech forests

The consequences of climate change continue to threaten European forests, particularly for species located at the edges of their latitudinal and altitudinal ranges. While extensively studied in Central Europe, European beech forests require further investigation to understand how climate change will affect these ecosystems in Mediterranean areas. Proposed silvicultural options increasingly aim at sustainable management to reduce biotic and abiotic stresses and enhance these forest ecosystems resistance and resilience mechanisms. Process-based models (PBMs) can help us to simulate such phenomena and capture early stress signals while considering the effect of different management approaches. In this study, we focus on estimating sensitivity of two state-of-the-art PBMs forest models by simulating carbon and water fluxes at the stand level to assess productivity changes and feedback resulting from different climatic forcings as well as different management regimes. We applied the 3D-CMCC-FEM and MEDFATE forest models for carbon (C) and water (H2O) fluxes in in two sites of the Italian peninsula, Cansiglio in the north and Mongiana in the south, under managed vs. unmanaged scenarios and under current climate and different climatic scenarios (RCP4.5 and RCP8.5). To ensure confidence in the models results, we preliminary evaluated their performance in simulating C and H2O flux in three additional beech forests of the FLUXNET network along a latitudinal gradient spanning from Denmark to central Italy. The 3D-CMCC-FEM model achieved R{superscript 2} values of 0.83 and 0.86 with RMSEs of 2.53 and 2.05 for C and H2O fluxes, respectively. MEDFATE showed R{superscript 2} values of 0.76 and 0.69 with RMSEs of 2.54 and 3.01. At the Cansiglio site in northern Italy, both models simulated a general increase in C and H2O fluxes under the RCP8.5 climate scenario compared to the current climate. Still, no benefit in managed plots compared to unmanaged ones, as the site does not have water availability limitations, and thus, competition for water is low. At the Mongiana site in southern Italy, both models predict a decrease in C and H2O fluxes and sensitivity to the different climatic forcing compared to the current climate; and an increase in C and H2O fluxes when considering specific management regimes compared to unmanaged scenarios. Conversely, under unmanaged scenarios plots are simulated to experience first signals of mortality prematurely due to water stress (MEDFATE) and carbon starvation (3D-CMCC-FEM) scenarios. In conclusion, while management interventions may be considered a viable solution for the conservation of beech forests under future climate conditions at moister sites like Cansiglio, in drier sites like Mongiana conservation may not lie in management interventions alone.

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↗