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Increasing tree diversity is considered a key management option to adapt forests to climate change. However, the effect of species diversity on a forest's ability to cope with extreme drought remains elusive. In this study, we assessed drought tolerance (xylem vulnerability to cavitation) and water stress (water potential), and combined them into a metric of drought-mortality risk (hydraulic safety margin) during extreme 2021 or 2022 summer droughts in five European tree diversity experiments encompassing different biomes. Overall, we found that drought-mortality risk was primarily driven by species identity (56.7% of the total variability), while tree diversity had a much lower effect (8% of the total variability). This result remained valid at the local scale (i.e within experiment) and across the studied European biomes. Tree diversity effect on drought-mortality risk was mediated by changes in water stress intensity, not by changes in xylem vulnerability to cavitation. Significant diversity effects were observed in all experiments, but those effects often varied from positive to negative across mixtures for a given species. Indeed, we found that the composition of the mixtures (i.e., the identities of the species mixed), but not the species richness of the mixture per se, is a driver of tree drought-mortality risk. This calls for a better understanding of the underlying mechanisms before tree diversity can be considered an operational adaption tool to extreme drought. Forest diversification should be considered jointly with management strategies focussed on favouring drought-tolerant species., (© 2024 John Wiley & Sons Ltd.)

Forests play a crucial role in the global carbon (C) cycle by storing and sequestering a substantial amount of C in the terrestrial biosphere. Due to temporal dynamics in climate and vegetation activity, there are significant regional variations in carbon dioxide (CO2) fluxes between the biosphere and atmosphere in forests that are affecting the global C cycle. Current forest CO2 flux dynamics are controlled by instantaneous climate, soil, and vegetation conditions, which carry legacy effects from disturbances and extreme climate events. Our level of understanding from the legacies of these processes on net CO2 fluxes is still limited due to their complexities and their long-term effects. Here, we combined remote sensing, climate, and eddy-covariance flux data to study net ecosystem CO2 exchange (NEE) at 185 forest sites globally. Instead of commonly used non-dynamic statistical methods, we employed a type of recurrent neural network (RNN), called Long Short-Term Memory network (LSTM) that captures information from the vegetation and climate's temporal dynamics. The resulting data-driven model integrates interannual and seasonal variations of climate and vegetation by using Landsat and climate data at each site. The presented LSTM algorithm was able to effectively describe the overall seasonal variability (Nash-Sutcliffe efficiency, NSE = 0.66) and across-site (NSE = 0.42) variations in NEE, while it had less success in predicting specific seasonal and interannual anomalies (NSE = 0.07). This analysis demonstrated that an LSTM approach with embedded climate and vegetation memory effects outperformed a non-dynamic statistical model (i.e. Random Forest) for estimating NEE. Additionally, it is shown that the vegetation mean seasonal cycle embeds most of the information content to realistically explain the spatial and seasonal variations in NEE. These findings show the relevance of capturing memory effects from both climate and vegetation in quantifying spatio-temporal variations in forest NEE., Competing Interests: The authors have declared that no competing interests exist.

Background and Aims: The structure of heterogeneous forests has consequences for their biophysical environment. Variations in the local climate significantly affect tree physiological processes. We hypothesize that forest structure also alters tree root elongation and longevity through temporal and spatial variations in soil temperature and water potential., Methods: We installed rhizotrons in paired vegetation communities of closed forest (tree islands) and open patches (canopy gaps), along a soil temperature gradient (elevations of 1400, 1700 and 2000 m) in a heterogeneous mixed forest. We measured the number of growing tree roots, elongation and mortality every month over 4 years., Key Results: The results showed that the mean daily root elongation rate (RER) was not correlated with soil water potential but was significantly and positively correlated with soil temperature between 0 and 8 °C only. The RER peaked in spring, and a smaller peak was usually observed in the autumn. Root longevity was dependent on altitude and the season in which roots were initiated, and root diameter was a significant factor explaining much of the variability observed. The finest roots usually grew faster and had a higher risk of mortality in gaps than in closed forest. At 2000 m, the finest roots had a higher risk of mortality compared with the lower altitudes., Conclusions: The RER was largely driven by soil temperature and was lower in cold soils. At the treeline, ephemeral fine roots were more numerous, probably in order to compensate for the shorter growing season. Differences in soil climate and root dynamics between gaps and closed forest were marked at 1400 and 1700 m, but not at 2000 m, where canopy cover was more sparse. Therefore, heterogeneous forest structure and situation play a significant role in determining root demography in temperate, montane forests, mostly through impacts on soil temperature.

We aimed to evaluate the importance of modulations of within-tree carbon (C) allocation by water and low-temperature stress for the prediction of annual forest growth with a process-based model. A new C allocation scheme was implemented in the CASTANEA model that accounts for lagged and direct environmental controls of C allocation. Different approaches (static vs dynamic) to modelling C allocation were then compared in a model-data fusion procedure, using satellite-derived leaf production estimates and biometric measurements at c. 10 4 sites. The modelling of the environmental control of C allocation significantly improved the ability of CASTANEA to predict the spatial and year-to-year variability of aboveground forest growth along regional gradients. A significant effect of the previous year's water stress on the C allocation to leaves and wood was reported. Our results also are consistent with a prominent role of the environmental modulation of sink demand in the wood growth of the studied species. Data available at large scales can inform forest models about the processes driving annual and seasonal C allocation. Our results call for a greater consideration of C allocation drivers, especially sink-demand fluctuations, for the simulations of current and future forest productivity with process-based models., (© 2016 The Authors. New Phytologist © 2016 New Phytologist Trust.)

Background and Aims: In mountain ecosystems, predicting root density in three dimensions (3-D) is highly challenging due to the spatial heterogeneity of forest communities. This study presents a simple and semi-mechanistic model, named ChaMRoots, that predicts root interception density (RID, number of roots m(-2)). ChaMRoots hypothesizes that RID at a given point is affected by the presence of roots from surrounding trees forming a polygon shape., Methods: The model comprises three sub-models for predicting: (1) the spatial heterogeneity - RID of the finest roots in the top soil layer as a function of tree basal area at breast height, and the distance between the tree and a given point; (2) the diameter spectrum - the distribution of RID as a function of root diameter up to 50 mm thick; and (3) the vertical profile - the distribution of RID as a function of soil depth. The RID data used for fitting in the model were measured in two uneven-aged mountain forest ecosystems in the French Alps. These sites differ in tree density and species composition., Key Results: In general, the validation of each sub-model indicated that all sub-models of ChaMRoots had good fits. The model achieved a highly satisfactory compromise between the number of aerial input parameters and the fit to the observed data., Conclusions: The semi-mechanistic ChaMRoots model focuses on the spatial distribution of root density at the tree cluster scale, in contrast to the majority of published root models, which function at the level of the individual. Based on easy-to-measure characteristics, simple forest inventory protocols and three sub-models, it achieves a good compromise between the complexity of the case study area and that of the global model structure. ChaMRoots can be easily coupled with spatially explicit individual-based forest dynamics models and thus provides a highly transferable approach for modelling 3-D root spatial distribution in complex forest ecosystems., (© The Author 2015. Published by Oxford University Press on behalf of the Annals of Botany Company. All rights reserved. For Permissions, please email: journals.permissions@oup.com.)