Deadwood is a key old-growth element in European forests and a cornerstone of biodiversity conservation practices in the region, recognized as an important indicator of sustainable forest management. Despite its importance as a legacy element for biodiversity, uncertainties remain on the drivers of deadwood potentials, its spatial distribution in European forests and how it may change in the future due to management and climate change. To fill this gap, we combined a comprehensive deadwood dataset to fit a machine learning and a Bayesian hurdle-lognormal model against multiple environmental and socio-economic predictors. We deployed the models on the gridded predictors to forecast changes in deadwood volumes in Europe under alternative climate (RCP4.5 and RCP8.5) and management scenarios (biodiversity-oriented and production-oriented strategies). Our results show deadwood hotspots in montane forests of central Europe and unmanaged forests in Scandinavia. Future climate conditions may reduce deadwood potentials up to 13% under a mid-century climate, with regional losses amounting to up to 22% in Southern Europe. Nevertheless, changes in management towards more biodiversity-oriented strategies, including an increase in the share of mixed forests and extended rotation lengths, may mitigate this loss to a 4% reduction in deadwood potentials. We conclude that adaptive management can promote deadwood under changing environmental conditions and thereby support habitat maintenance and forest multifunctionality.
Keywords: Biodiversity; Climate change; Deadwood; Ecosystem services; Forest management.
Copyright © 2024 The Authors. Published by Elsevier Ltd.. All rights reserved.