Pedoclimatic classification of Europe as a basis for site-appropriate tree species selection in climate change

Information about soils and climate of the origin regions of the tree species is needed for the “assisted migration” of genotypes better adapted to the future climate as well as for planting of other native and non-native but better adapted tree species. Ecological classifications are seen as an important basis for “assisted migration”. However, up to now, all known approaches to classify regions of tree origin do not take into account soil properties, although soil is important for the selection of suitable origins of seed and planting material. The presented pedological-climatic classification of Central and Southern Europe can serve as a basis for a unified classification of forest growth regions that integrates soil properties for the first time. For the soil parameter of the pedoclimatic classification, the WRB soil map from the European Soil Data Centre 1:1.000.000 was used and classified for the base status of the soils. The climate was characterized by the water deficit in summer and a winter hardiness parameter. For this purpose, the CHELSA datasets for the periods 1981–2010, 2011–2040 and 2071–2100 were used, the latter with the SSP585 scenario. This gives us a 3 × 5 × 3 matrix with 45 units, with which we can fairly well map all seed and plant origins of forest tree species in Central and Southern Europe.

Authors

Institutions

Publication Details

Journal
European Journal of Forest Research
Published
2026-03-11
DOI
https://doi.org/10.1007/s10342-026-01890-w
Primary Topic
Species Distribution and Climate Change
Type
article
Field-Weighted Citation Impact
0.00
Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

Pedoclimatic classification of Europe as a basis for site-appropriate tree species selection in climate change

E Kolb, Axel Göttlein
European Journal of Forest Research
Species Distribution and Climate Change
article

Pedoclimatic classification of Europe as a basis for site-appropriate tree species selection in climate change

E Kolb, Axel Göttlein
article en

Abstract

Information about soils and climate of the origin regions of the tree species is needed for the “assisted migration” of genotypes better adapted to the future climate as well as for planting of other native and non-native but better adapted tree species. Ecological classifications are seen as an important basis for “assisted migration”. However, up to now, all known approaches to classify regions of tree origin do not take into account soil properties, although soil is important for the selection of suitable origins of seed and planting material. The presented pedological-climatic classification of Central and Southern Europe can serve as a basis for a unified classification of forest growth regions that integrates soil properties for the first time. For the soil parameter of the pedoclimatic classification, the WRB soil map from the European Soil Data Centre 1:1.000.000 was used and classified for the base status of the soils. The climate was characterized by the water deficit in summer and a winter hardiness parameter. For this purpose, the CHELSA datasets for the periods 1981–2010, 2011–2040 and 2071–2100 were used, the latter with the SSP585 scenario. This gives us a 3 × 5 × 3 matrix with 45 units, with which we can fairly well map all seed and plant origins of forest tree species in Central and Southern Europe.

European Journal of Forest ResearchVol. 145(2)
Technical University of Munich (DE)
Climate action
Openalex Percentile: Top 30%
Species Distribution and Climate Change
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.