Modelling the effects of gradual climate change and extreme events on diameter growth
PhD student
Ysaline PerraudResearch Outline
This project aims to model the compositional and structural effects on tree growth in response to gradual climate change and extreme events, focusing on pure and mixed stands of European beech, Norway spruce, and Douglas-fir. With an increasing frequency of extreme events due to climate change, such as droughts and storms, understanding tree reactions becomes crucial for forest health and resilience. The project addresses gaps in knowledge regarding the interactions between species mixtures, stand productivity, and resilience to environmental stressors.The primary project objectives include extending the diameter increment function of the TreeGrOSS growth model for the selected species by incorporating climate and soil variables, exploring the differences between gradual climate change and extreme events, relating species growth dynamics to functional traits, and projecting forest development under various scenarios. We will enhance the TreeGrOSS model by incorporating climate effects, soil variables, and management using GAMs and functional regressions. With GAMs we will analyze diameter increments of research plots to show gradual climate change effects, while functional regression models will be used to assess immediate short-term impacts of extreme events using increment cores and annual diameter measurements. A trait-based approach will also be employed to analyze tree species traits, such as physiological and morphological characteristics, and relate them to specific growth dynamics, aiming to generalize species-specific growth functions. The final goal involves projecting responses of our research plots to different management scenarios, changing climate, and extreme events, addressing trade-offs in ecosystem services.
This comprehensive approach aims to enhance the precision of growth models, accounting for the impacts of climate change and extreme events. The project's findings are essential to provide insights into adaptive strategies, and to inform effective silvicultural management strategies in a changing environment, ensuring sustainable resource utilization and safeguarding forests.
Key words: Statistical modelling, climate change, diameter growth, extreme events, Generalized Additive Models, Functional regressions, Trait-based generalisation