Combining statistical and mechanistic models to unravel the drivers of mortality within a rear-edge beech population

As several studies report an increasing decline of forests, a major issue in ecology is to better understand and predict tree mortality. The interactions between the different factors and physiological processes driving tree mortality, as well as the individual between-trees variability of mortality risk remain to be characterised. This study is based on an exceptional yearly individual monitoring of 4327 European beeches (Fagus sylvatica) since 2002 in a rear-edge population within a natural reserve. We combined two types of approaches. Statistical models were used to quantify the effects of climate, competition, tree size and health on mortality. Carbon reserves, hydraulic conductance and late frosts were simulated using a process-based model to disentangle the mechanisms driving temporal and inter-individual variations in mortality. The mortality rate at population level was associated to drought indices in statistical models, and driven by a combination of conductance loss, carbon reserve depletion and late frost damages in the process-based simulations. In statistical models, the individual probability of mortality decreased with mean growth, and increased with crown defoliation, budburst earliness, fungi presence and competition. Interaction effects between tree size and defoliation were significant, the probability of mortality being higher for a small-defoliated tree than for a tall one. Finally, the process-based model predicted a higher conductance loss and a higher frequency of late frosts for earlier trees together with a higher level of carbon reserve, while the ability to defoliate crown was found to limit the impact of hydraulic stress and allow carbon reserve accumulation. We discuss the convergences and divergences obtained between statistical and process-based approaches and we highlight the importance of combining them to disentangle the processes underlying mortality, and to account for individual variability in vulnerability.