Écologie des Forêts Méditerranéennes

Accurately describing forest ecosystem responses to varying climatic conditions—particularly water availability—requires a robust representation of the soil–plant–atmosphere continuum. Recent advances have produced high-fidelity models for both surface–subsurface hydrology and plant hydraulics. Although these model domains partially overlap in the processes they consider, their implementation is often inadequate compared with their counterparts. To address this limitation, we present a coupled version of two established models: the integrated surface–subsurface flow model SERGHEI-SWE-RE and the plant hydraulics model SurEau-Ecos.SERGHEI-SWE-RE is a performance-portable, high-performance parallel computing model that solves the fully dynamic two-dimensional shallow-water equations for surface flow and the three-dimensional Richards equation for subsurface flow. SurEau-Ecos is a mechanistic, trait-based plant hydraulics model that provides a detailed physiological description of plant water status beyond stomatal closure up to the point of hydraulic failure. The core strengths of both models are coupled through a clean separation at the soil–root interface: SERGHEI-SWE-RE supplies spatially distributed soil water potential fields, while SurEau-Ecos provides the resulting root water uptake. Leveraging the high-performance computing capabilities of SERGHEI-SWE-RE, instances of SurEau-Ecos can be mapped to each node of the surface mesh and the corresponding vertical soil column. This coupled model—spatially distributed and operating at high temporal resolution—captures hydrodynamic processes in complex geometries, such as lateral subsurface flow, exfiltration, ponding, and deep water reserves, while simultaneously enabling the assessment of forest ecosystem responses, such as drought stress and tree dieback.We present a proof-of-concept version of the coupled model using a transect along an idealised vegetated hillslope, where lateral subsurface fluxes are a key process. The effects of subsurface flow concentration toward the valley on plant-available water are shown and the resulting duration over which trees can sustain drought conditions are analysed.

Le gui, Viscum album, parasite de nombreuses espèces arborées, incluant certaines essences majeures des forêts françaises. L’Inventaire forestier national collecte, depuis 2008, des données concernant la présence et l’abondance de boules de gui sur les arbres des placettes inventoriées annuellement. En nous appuyant sur ces observations, nous présentons ici un état des lieux du gui dans les forêts françaises. Plus particulièrement, nous nous intéressons à la répartition géographique du parasite ainsi qu’à son évolution temporelle. Nous explorons également les facteurs, tant environnementaux que propres aux peuplements ou aux arbres individuels, favorisant la présence du gui. Messages clés : Trois sous-espèces de gui sont présentes en France hexagonale et en Corse, qui se distinguent par les essences ligneuses parasitées : gui des feuillus, sur tout le territoire ; gui du pin, en forte proportion dans les Alpes ; gui du sapin, particulièrement présent dans les forêts vosgiennes. Le Peuplier cultivé, le Robinier faux-acacia et le Pin sylvestre sont, en termes de taux d’incidence, les principales essences hôtes du gui en France ; le Sapin pectiné, les Saules, les Petits érables et les arbres fruitiers étant d’autres essences fréquemment guitées. Les arbres présentant une proportion importante de branches mortes dans la partie supérieure de leur houppier sont ceux sur lesquels le gui a été observé le plus fréquemment.

The decline in bee populations is influenced by the scarcity of floral resources, mainly nectar and pollen, resulting from the loss of natural habitats and climate change. In this context, it is essential to understand the foraging success and nutritional status of wild bees, particularly as research on honeybees has overshadowed that of wild species. This study aims to establish allometric functions for the nectar crop capacities of wild bees, which may be used as predictive tools in research related to bee nutrition and foraging ecology. Allometric scaling models, which typically relate body size to morphological or ecological traits, are proposed as a method of estimating crop capacity in wild bees. The crop load was measured in 2358 free-ranging foragers of different sizes (body length 5-29mm) belonging to 25 different genera, in southern France. Overall, we established consistent allometric functions for predicting crop load capacities from the body length of wild bees, with a satisfactory degree of confidence. Sex is an important determinant of crop capacity, with females carrying maximal nectar loads around three times greater than males of similar size. We also found evidence that taxonomic affiliation influences allometric properties of crop capacities. Further studies are however needed to refine the predictions by taxonomic group, and in particular for the eusocial bumblebees. Field validation involving complementary data on floral nectar availability indicated that an allometric framework can be used to address research questions related to foraging success and competition for nectar in large wild bees (body length similar or greater than that of honeybees). Future studies should aim at refining the taxonomic resolution of allometric functions to family or genus level, which would provide a better understanding of the ecological particularities of certain groups of bees, such as eusocial species whose foragers store nectar for the entire colony or, conversely, cleptoparasitic (cuckoo) female bees that do not supply their broods.

Forests cover almost one third of the Earth's land area and are central in the carbon and water cycles. Soil water availability is one of the most important factors regulating transpiration, biomass production and plant species distribution in ecosystems. The carbon and water cycles are closely linked and so understanding the functioning and evolution of forest environments and their relation to subsurface structure and water availability is essential to improve understanding of the water cycle under a changing climate. Studying the forest subsurface is a challenge because of its heterogeneous nature and difficult accessibility. Traditional approaches used by ecologists are also often point measurements that have a low spatial representativity. Near-surface geophysics offers a wide range of methods to characterize the spatial and temporal variability of subsurface properties and associated processes in a non-destructive and integrative way. Geophysical methods allow us to obtain new information that complements ecophysiological methods to better understand ecosystem functioning, and in particular processes linked to ecohydrology. The use of geophysical methods in forests is growing, both by geophysicists seeking to apply their tools to more complex environments, and by ecologists seeking to better characterize their experimental sites. One of the major applications and assets of geophysics in forests is to quantify and monitor water stocks and dynamics. For example, geoelectrical monitoring can be used to assess the distribution and spatial variations of water content in the subsoil. In this work, we show the example of a recently developed ensemble approach to quantitatively relate electrical conductivity monitoring and the distribution and dynamic of water in forest soils. We believe that such interdisciplinary advances can help us improving the quantitative assessment of forest responses to the environment and their adaptation to climate change.

Groundwater plays a key role in providing access to drinking water, especially in semi-arid regions where surface water is scarce or absent for much of the year. In the semi-arid region of southern Madagascar, approximately 2,000,000 people face one of the highest poverty rates in the world, making them particularly vulnerable to climatic hazards. As a result, describing and predicting groundwater dynamics is essential to understand and anticipate drought-related humanitarian crises. How to estimate groundwater recharge in a such poorly documented area?Our work consisted of comparing two complementary approaches for estimating groundwater recharge. First, the Groundwater Resource Observatory for Southwestern Madagascar was established in 2014 in difficult logistical settings to monitor piezometric level from 16 boreholes located in various hydrogeological systems. This observatory provides long-term piezometric time series at an hourly time step, which were used to calculate recharge following the Water Table Fluctuation (WTF) Method.Second, a spatial hydrology approach was developed to estimate potential recharge using precipitation and evapotranspiration global products based on remote sensing data. The two approaches were compared, revealing the potential and limits of both. Based on these results, we compare our findings with health outcomes, offering new avenues for research.

BackgroundWeather conditions play a crucial role in driving fire activity in Mediterranean France. Previous research has demonstrated the influence of these conditions on the likelihood of large fire events over the world. However, certain limitations persist regarding the representation of fire weather in probabilistic models. AimThe objective of this paper is to develop an efficient method to rate fire danger by identifying the best representation of weather data for fire activity prediction in Mediterranean France. MethodsWe evaluated the performance of meteorological variables and the most common fire-weather indices (FWIs) worldwide as predictors of fire occurrence and size using the Firelihood framework, a probabilistic Bayesian model of fire activity. These models were compared to a fire activity baseline model incorporating only spatial and temporal effects but no explicit fire-weather information to allow for an in-depth study of information not captured by fire-weather indices. Key resultsThe results indicate that relying solely on fire-weather indices is insufficient for efficient rating of fire activities. The inclusion of spatial and seasonal effects in the models is crucial for improving the indices' performance. While the Canadian FWI remains the most skillful indicator among the tested indices, using new combinations of several of its subcomponents further increases accuracy. Various performance analyses, including threshold selections, were carried out to comparatively assess those improvements. ConclusionsThe approach shows that probabilistic models informed with appropriately constructed fire-weather indices substantially improve various aspects of fire activity predictions in the Mediterranean area.

<strong class="journal-contentHeaderColor">Abstract.</strong> Global radiation is a key climate input in forest process-based models (PBM) as it determines photosynthesis, transpiration and the canopy energy balance. While radiation is highly variable at fine spatial resolution in complex terrain due to shadowing effects, data required for PBM currently available over large extents are generally at spatial resolution coarser than ~9 km. Downscaling radiation from large-scale to high resolution available from digital elevation models is therefore of potential importance to refine global radiation estimates and improve PBM estimations. In this study, we introduce a new downscaling model that aims to refine sub-daily global radiation data obtained from climate reanalysis or projection at large scales to the resolution of a given digital elevation model. First, downscaling involves splitting radiation into direct and diffuse fraction. Then, the influence of surrounding mountains' shade on direct radiation and the &ldquo;bowl&rdquo; (deep valley) effect on diffuse radiation is considered. The model was evaluated by comparing simulated and observed radiation at the Mont Ventoux mountain study site (southeast of France) using the recent ERA5-Land hourly data available at 9 km resolution as input and downscaled at different spatial resolution (from 1 km to 30 m resolution) using a digital elevation model. The downscaling algorithm improved the reliability of radiation at the study site in particular at scales below 150 m. Finally, by using two different process based models (Castanea, a process-based model simulating tree growth, and SurEau, a plant-hydraulic model simulating hydraulic failure risk), we showed that accounting for fine resolution radiation can have a great impact on predictions of forest functions and climatic risks.