Understanding how European forests will respond to global change requires integrating ectomycorrhizal symbioses into forest management, ecological models, and restoration. Achieving this requires a fundamental understanding of the effects of ectomycorrhizal fungi on forest processes and sensitivities to global change. In this talk, I will discuss links between ectomycorrhizal fungal communities (i.e., biodiversity, traits, genomic features) and forest tree growth, nutrient uptake, and responses to climate change. I will share insights from my recent observational studies of ectomycorrhizal fungi across forest monitoring plots and new discoveries from our climate change experiments where we manipulate ectomycorrhizal fungal communities to study host tree and soil biogeochemical responses to warming and drought.

The proliferation of presence-only data from diverse sources—ranging from citizen science initiatives to automated sensor networks—presents both opportunities and challenges for spatial modelling in ecology and related fields. This work presents a unified methodological framework for integrating such heterogeneous data using spatial point process models, with a focus on Log-Gaussian Cox Processes (LGCPs). These models provide a flexible means of capturing complex spatial and temporal variability by leveraging the properties of Gaussian random fields. Estimation is carried out in a Bayesian setting using the Integrated Nested Laplace Approximation (INLA), enhanced with a barrier model to account for physical environmental constraints and spatial discontinuities To address sampling biases and observation uncertainty inherent in presence-only data, we adopt the concept of thinned point processes, where observed occurrences represent a filtered realization of an underlying intensity function modulated by a detection function. This framework enables the robust merging of data collected under varying protocols and observational efforts. Applications span marine ecology, with case studies on dolphins, white sharks, and sea cucumbers in the Mediterranean Sea, showcasing the framework’s adaptability and potential for advancing species distribution modeling and conservation planning. In particular, sea cucumbers will be analysed with some attention. In this study, we combine automatic object detection techniques with point processes.

Accurate uncertainty quantification is critical in climate science, especially when it comes to rare but high-impact events. One common approach is to use ensembles of physical climate model simulations—but running these models is computationally intensive, often requiring supercomputers and many hours for a single simulation. This makes it difficult to quickly explore uncertainty in a wide range of possible scenarios.
In this talk, I present a statistical emulator designed to address this bottleneck. Using a small number of high-dimensional samples from a physical model, we build a flexible model of the joint distribution of climate variables—such as daily precipitation across the globe—and use it to efficiently generate additional samples. Our approach combines a structured spatial model for dependence with flexible marginal models that can handle skewed, heavy-tailed, or even multimodal data.
The key idea is to transform the original data into a simpler Gaussian representation using a three-stage composition: a parametric and semiparametric marginal transformation, followed by a scalable Bayesian transport map for modeling spatial dependence. This setup allows us to emulate complex climate model output with drastically reduced data requirements and computation time.
We demonstrate the method on simulated global precipitation data, showing that it achieves higher accuracy with 20 samples than previous methods did using 80 samples. The emulator offers a practical path to more efficient climate uncertainty analysis while closely capturing the behavior of physical models.

The relation between Site Index and the actual volume increment of forest stands is subject to regional variations. This observation was already made in the 1970s by ASSMANN, who described it as the subdivided specialized yield level. To this day, it has only be described empirically. Our goal was a causal description of the subdivided specialized yield level based on experimental plot data and covariates for short term climate within a generalized additive model framework.

Understanding tree growth dynamics is crucial in economic, ecological, and political contexts and has implications for forest development scenario analyses and management strategies. Traditional empirical models for basal area increment (BAI) typically rely on basic tree, stand, and site information, assuming constant environmental conditions. Recognizing the urgency to incorporate (a) effects of environmental change and (b) more complex stand dynamics, the BAI modelling based on forty years of Swiss National Forest Inventory (NFI) data has undergone a variety of advancements over the recent years.
Regarding environmental change, initial efforts focused on incorporating temperature, drought, and N-deposition effects into empirical BAI models using a space-for-time substitution approach. We optimized the definition of climatic variables by complementing NFI with tree-ring data. Further model developments specifically focused on interactions, particularly between drought and N-deposition. Additionally, while studying the effects of the severe 2018 drought on a sub-selection of NFI plots, we found that compared to decennial intervals between inventories, annual remeasurements improved the quantification of drought effects on growth.
Regarding more complex stand dynamics, we found that mixed forests can promote BAI compared to monospecific stands, depending upon stand, topographic, climatic, and soil conditions. Furthermore, by combining NFI data with growth and yield data, growth partitioning was investigated, i.e., the relative contribution of differently sized trees to total stand growth. Currently, our research makes use of the long-term nature of NFI data, which provides a unique opportunity to study temporal growth trends at regional and national scales, providing crucial insights into global change effects.
Our research revealed the great potential of NFI data for empirically modelling tree growth dynamics, as well as pathways to complement NFI with other data sources for further improvement of targeted growth-related research.

Earth's forests face grave challenges in the Anthropocene, including hotter droughts increasingly associated with widespread forest die-off events. But despite the vital importance of forests to global ecosystem services, their fates in a warming world remain highly uncertain. Lacking is quantitative determination of commonality in climate anomalies associated with pulses of tree mortality-from published, field-documented mortality events-required for understanding the role of extreme climate events in overall global tree die-off patterns. Here we established a geo-referenced global database documenting climate-induced mortality events spanning all tree-supporting biomes and continents, from 154 peer-reviewed studies since 1970. The analysis quantifies a global "hotter-drought fingerprint" from these tree-mortality sites-effectively a hotter and drier climate signal for tree mortality-across 675 locations encompassing 1,303 plots. Frequency of these observed mortality-year climate conditions strongly increases nonlinearly under projected warming.

Resource use by consumers across patches is often proportional to the quantity or quality of the resource within these patches. In folivores, such proportional use of resources is likely to be more efficient when plants are spatially proximate, such as trees forming a forest canopy. However, resources provided by forest-trees are often not used proportionally. We hypothesised that proportional use of resources is reduced when host trees are isolated among phylogenetically distant neighbours that mask olfactory and visual search cues, and reduce folivore movement between trees. Such phylogenetically distant neighbourhoods might sort out species that are specialists, poor dispersers, or have poor access to information about leaf quality. We studied individual oaks, their leaf size and quality, their folivory and abundance of folivores (mostly Lepidopteran ectophages, gallers and miners), and parasitism of folivores. We found that leaf consumption by ectophages hardly increased with increasing leaf size when host trees were phylogenetically isolated. We found a similar effect on host use by parasitoids in 1 year. In contrast, we found no consistent effects in other folivore guilds. Relative abundances of specialists and species with wingless females declined with phylogenetic isolation. However, resource use within each of these groups was inconsistently affected by phylogenetic isolation. We suggest that phylogenetic isolation prevents ectophages from effectively choosing trees with abundant resources, and also sorts out species likely to recruit in situ on their host tree. Trees in phylogenetically distant neighbourhoods may be selected for larger leaves and greater reliance on induced defences.

Large-scale forest development simulations are invaluable tools that harness mathematical models, computer algorithms, and extensive datasets to forecast the evolution of forest ecosystems across extended periods of time. Their applications span a diverse spectrum, ranging from informing forest policies and projecting the supply of wood and other ecosystem services (such as biodiversity and recreation) to facilitating sustainable forest management strategies. However, these sophisticated tools can often appear as enigmatic 'black boxes' to those not intimately familiar with them. In this presentation, I will illuminate the inner workings of these simulations, shedding light on their foundational assumptions and models. We will also explore the synergies between forest simulations and various domains within forestry research, demonstrating how simulations can significantly amplify the impact of research across these domains.

Forest biodiversity underpins ecosystem functioning and the provision of multiple ecosystem goods and services that are essential to human well-being. Still, the social value of biodiversity is rarely considered in the management of forest resources, potentially compromising ecosystem functioning and social welfare in the future. Moreover, climate change and other sources of uncertainty add further complexity to the implementation of biodiversity-oriented forest management. These multiple uncertainties are expected to affect the interactions among forest taxa and the relationships between forests and the socio-economic context in which they are embedded. Consequently, uncertainty needs to be addressed in the decision-making process and conservation planning, seeking for robust alternatives that safeguard forest biodiversity regardless of future states of the world. Here we discuss approaches to tackle these issues, analyzing economic aspects of biodiversity-oriented management, with a focus on retention forestry, and the socially optimal biodiversity supply in temperate forests under multiple sources of uncertainty.