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Research article2023Peer reviewedOpen access

Modelling Dominant Tree Heights of Fagus sylvatica L. Using Function-on-Scalar Regression Based on Forest Inventory Data

Engel, Markus; Mette, Tobias; Falk, Wolfgang; Poschenrieder, Werner; Fridman, Jonas; Skudnik, Mitja


European beech (Fagus sylvatica L.) is an important tree species throughout Europe but shifts in its suitable habitats are expected in the future due to climate change. Finding provenances that are still economically viable and ecologically resilient is an ongoing field of research. We modelled the dominant tree heights of European beech as a trait reflecting growth performance dependent on provenance, climate and soil conditions. We derived dominant tree heights from national forest inventory (NFI) data from six European countries spanning over large ecological gradients. We performed function-on-scalar regression using hierarchical generalized additive models (HGAM) to model both the global effects shared among all provenances and the effects specific to a particular provenance. By comparing predictions for a reference period of 1981-2010 and 2071-2100 in a RCP 8.5 scenario, we showed that changes in growth performance can be expected in the future. Dominant tree heights decreased in Southern and Central Europe but increased in Northern Europe by more than 10 m. Changes in growth performance were always accompanied by a change in beech provenances, assuming assisted migration without dispersal limitations. Our results support the concept of assisted migration for the building of resilient future forests and emphasize the use of genetic data for future growth predictions.


hierarchical GAMs; functional regression; Fagus sylvatica; provenance; assisted migration

Published in

2023, Volume: 14, number: 2, article number: 304
Publisher: MDPI

    Sustainable Development Goals

    SDG13 Climate action

    UKÄ Subject classification

    Forest Science

    Publication identifier


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